Student thesis series INES nr 466

Sigurbjörn Bogi Jónsson

RGB and Multispectral UAV image classification

of agricultural fields using a machine learning

algorithm

2018

Department of

Physical Geography and Ecosystem Science

Lund University

Sölvegatan 12

S-223 62 Lund

Sweden

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Sigurbjörn Bogi Jónsson (2018).

RGB and Multispectral UAV image classification of agricultural fields using a

machine learning algorithm

Master degree thesis, 30 credits in Geomatics

Department of Physical Geography and Ecosystem Science, Lund University

Level: Master of Science (MSc)

Course duration: January 2018 until June 2018

Disclaimer

This document describes work undertaken as part of a program of study at the

University of Lund. All views and opinions expressed herein remain the sole

responsibility of the author, and do not necessarily represent those of the institute.

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RGB and Multispectral UAV image classification of

agricultural fields using a machine learning

algorithm

Sigurbjörn Bogi Jónsson

Master thesis, 30 credits, in Geomatics

Lars Eklundh

Lund University, Department of Physical Geography and Ecosystem

Science

Perola Olsson

Lund University, Department of Physical Geography and Ecosystem

Science

Exam committee:

Torbern Tagesson and Jonas Ardö, Lund University,

Department of Physical Geography and Ecosystem Science

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Abstract

A common technique within image analysis is image classification which describes the

process of reducing the information content of an image into few user-defined classes.

With the emergence of unmanned aerial vehicles (UAVs), high spatial resolution (cm-

level) images can be collected. This thesis aims at testing different methods for

classifying UAV images from an agricultural crop site in the south of Skåne, Sweden.

To classify the UAV images, the Random Forest algorithm was used. Random Forest

(RF) is an ensemble classifier which consists of multiple classification-trees, where the

results of each individual tree contributes a single vote for to which class each pixel or

segment belongs to. To evaluate the results a few objectives are presented. First of all,

two cameras (RGB and multispectral) were used to examine the effect of different

wavelengths bands on classification accuracy. Furthermore, the effects of spatial

resolution, segmentation and integration of additional data were tested. To evaluate

these different strategies a few classification examples were tested; two general

classification cases, a 5-class classification and 11-class classification, and one

specialized case where the high resolution of the UAV was used to classify a crop field

consisting of two crop types.

Both RGB and multispectral cameras performed well, reaching overall accuracies

greater than 75% for all classification cases. Results from the general cases show little

difference between RGB and multispectral cameras. However, in performing the

specialized case classification, i.e., analyzing a field containing two spectrally similar

classes, the multispectral camera outperformed the RGB. The pixel size has a big

impact on resulting classification accuracy for both RGB and multispectral cameras

(30% difference in accuracy ranging from 5 cm to 1 m pixel size), where higher

accuracies are achieved at higher spatial resolutions. By integrating addition data

sources in the pixel-by-pixel classification method, accuracies increase by a factor of

>10%. The Mean – texture feature turns out to be the most important texture feature for

both cameras. The highest accuracy, for both RGB and multispectral classification, was

achieved by classifying groups of pixels into segments, reaching overall accuracy of

>90%.

Overall, UAV image classification works well for agricultural farm mapping and is a

good monitoring tool due to its quick deployment, ease of data collection and accurate

results.

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Table of Contents

1. Introduction ............................................................................................................ 1

1.1. Objectives ........................................................................................................ 3

2. Background ............................................................................................................ 5

2.1. Remote sensing and the electromagnetic spectrum ......................................... 5

2.2. Image classification ......................................................................................... 6

2.3. Random Forest Classifier ................................................................................ 7

2.4. Texture analysis and Vegetation indices ......................................................... 8

2.5. Accuracy validation ......................................................................................... 9

3. Methodology ........................................................................................................ 11

3.1. Study area ...................................................................................................... 11

3.2. Data ............................................................................................................... 13

3.2.1. Field work – sampling method .............................................................. 14

3.3. Methods ......................................................................................................... 15

3.3.1. Information classes – Classification strategy ......................................... 15

3.3.1.1. General Case ................................................................................... 15

3.3.1.2. Specialized Case ............................................................................. 17

3.3.2. Wavelengths and spatial resolution ....................................................... 17

3.3.3. Segmentation .......................................................................................... 18

3.3.4. Integration of additional data ................................................................. 19

3.3.5. Accuracy Assessment ............................................................................ 19

3.3.6. Random Forest ....................................................................................... 20

4. Results .................................................................................................................. 21

4.1. General Case ................................................................................................. 21

4.1.1. 5-class classification .............................................................................. 21

4.1.2. 11-class classification ............................................................................ 24

4.2. Specialized Case ............................................................................................ 30

5. Discussion ............................................................................................................ 33

5.1. General Thoughts .......................................................................................... 33

5.2. Effects of spatial resolution, segmentation and additional features .............. 33

5.3. UAV pre- and postprocessing ....................................................................... 34

5.4. Accuracy evaluation ...................................................................................... 34

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6. Conclusion ........................................................................................................... 37

7. References ............................................................................................................ 39

8. Appendices ........................................................................................................... 41

8.1. Appendix A: Classification results, images. ................................................. 41

8.2. Appendix B: Classification results, confusion matrices. ............................... 67

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1. Introduction

In recent years, unmanned aerial vehicles (UAVs) have become a popular tool for

collecting remotely sensed data. Consequently, a wide range of UAV applications have

emerged within various scientific fields, from land cover mapping and environmental

monitoring to precise civil engineering surveys. A common application within

environmental science is the acquisition of aerial images, collected using either red-

green-blue (RGB) or multispectral cameras onboard the UAV. Aerial images contain

vast amount of information, thus allowing various interpretations to be made depending

on desired output. A common technique within image analysis is image classification

which describes the process of reducing the information content of an image into few

user-defined classes. In other words, every pixel in the original image is assigned a new

value, nominal or numeric, based on some defined classification criteria. The output

image is often named land-cover map and typically contains broad information classes

such as forest, water, crop fields, etc. (Özdogan, 2015).

Before the usage of UAVs for classifying remotely sensed images, satellites were (and

still are) the most commonly used sensor platform. There are several advantages of

using UAV images over satellite imagery, e.g. quick deployment, data can be collected

on demand and UAVs are more flexible in terms of flight height and angle (Rango et

al., 2009). Another key advantage over satellite imagery is that UAV images can be

collected in very high spatial resolution (cm-level). Spatial resolution corresponds to

the smallest identifiable surface object on the image and is often denoted as the pixel

size, even though pixels can be scaled to larger or lower sizes (Chuvieco, 2016). With

higher spatial resolution the broad information classes mentioned earlier (forest, crop

fields, etc.) can be classified even further. Therefore, more fine-scaled classification

can be performed allowing different crop types or different tree species to be detected,

as examples.

An environmental field that greatly benefits in having accurate land cover maps are

agricultural farmlands. The farming industry is highly sensitive of factors such as soil

condition, temperature, rainfall etc. and being able to monitor crop fields at regular

basis increases the chances of production optimization. UAV image classification can

play important role in this monitoring process. Having an accurate overview of the

croplands area and being able to map healthy soil and target weeds, as an example, are

factors which aid in decision making and can play a crucial role in future farming

strategies.

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How an image gets classified ultimately depends on its pixel’s values but also on which

classification method is chosen. Various classification algorithms are available, both

parametric and non-parametric classifiers. The non-parametric algorithms such as

Random Forest classifier (RF) and Support Vector Machine (SVM) generally produce

better results, with classification accuracy of 10%-20% higher than for parametric

classifiers (Özdogan, 2015). Another factor which influences the classification result is

whether each pixel in the original image is classified (pixel-by-pixel technique) or if

classifying group of pixels forming segments, based on pixel´s spectral similarity

(object-based image analysis), is chosen.

Another important factor in the classification process is the construction of the original

image, i.e. the number of bands which the image is made up from. A standard aerial

photo, captured by an RGB (red, green, blue) camera is made up from three bands;

namely the red, green and blue band which correspond to the visible part of the

electromagnetic spectrum (EM), the part which the human eye can detect. By utilizing

other parts of the electromagnetic spectrum, e.g. the infrared band or thermal band,

more information can be gathered for the surface object. Camera sensors that gather

spectral information in other parts of the EM than the visible part are named

multispectral cameras and cameras only capable of retrieving information from the

visible part are named RGB-cameras.

Although many articles and books have been published in the field of image

classification, there are many aspects not yet thoroughly investigated, especially for

UAV images. Comparing pixel-by-pixel and object-based image analysis (OBIA)

techniques has not been substantially reported for UAV images and comparing RGB to

multispectral UAV image classification is greatly lacking scientific review.

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1.1. Objectives

This thesis aims at testing various methods for classifying agricultural fields using UAV

images. The primary aim is to compare classification accuracies by using two different

cameras onboard the UAV, i.e. an RGB and a multispectral camera to assess the

importance of including wavelength bands from the non-visible part of the

electromagnetic spectrum. To evaluate the results, three additional objectives are

suggested below which aim to contribute to the ever-growing knowledge base of UAV

image classification by analyzing the effect of:

1) Spatial resolution. The effect of pixel size on the classification accuracy.

2) Segmentation. The pixel-by-pixel and segmentation classification processes

will be evaluated.

3) Integrating additional data to improve accuracy. In this study, digital elevation

model (DEM), texture features and vegetation indices are added as input

features in the classification algorithm and its effect on classification accuracy

documented.

For all cases, the Random Forest algorithm will be used as classifier, having been used

successfully for classifying UAV images (Baron, Hill, & Elmiligi, 2018; Franklin &

Ahmed, 2017).

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2. Background

2.1. Remote sensing and the electromagnetic spectrum

Remote sensing describes the process of collecting data without physically touching the

object of interest, i.e. data is collected remotely from a distance. In its most simple case,

sensors aboard various platforms (UAV, airborne, satellite, etc.) record electromagnetic

energy signals, which are either reflected or emitted, from ground objects and that

signal later gets transformed to useful information. Reflected energy describes how

utilizing sunlight as an energy source causes a portion of that energy to reflect or bounce

back into space to be received by the sensor.

Electromagnetic radiation can vary from very short to vey long wavelengths.

Consequently, the range of wavelengths has been classified into regions or spectral

bands, which are organized in the electromagnetic spectrum displayed in Figure 1

(Chuvieco, 2016). Shorter wavelengths (Gamma or X-rays) have higher frequencies

than longer wavelengths (Microwaves or Radio waves) and therefore have a higher

energy content (Chuvieco, 2016).

Figure 1: The electromagnetic spectrum and its spectral bands division. The visible spectrum defines the range

which the human eye can sense. (source: Figure modified from Chuvieco, 2016)

The visible and infrared bands are commonly used bands on UAV platforms. The

visible spectrum ranges from 0.4 to 0.7 μm and can be further divided into blue (0.4 -

0.5 μm), green (0.5 - 0.6 μm) and red (0.6 – 0.7 μm) bands. As the name suggests, the

visible band defines the range of the electromagnetic spectrum which the human eyes

can sense. The non-visible Infrared band ranges from 0.7 to 14 μm and can be divided

into near infrared (NIR), short wave infrared (SWIR) and thermal infrared (TIR) bands

(Chuvieco, 2016).

How electromagnetic energy is reflected, absorbed or emitted across various spectral

bands is governed by the ground surface and how it reacts to different energy levels.

Furthermore, the land cover type as well as its physical and chemical properties controls

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how the reflectance acts across the electromagnetic spectrum. This reflectance behavior

can be graphed, with reflectance on the y-axis and wavelengths on the x-axis, to

visualize the spectral “fingerprint” of the ground object. Figure 2 displays this kind of

graph, often called spectral signature, for 3 different land cover types; namely water,

vegetation and concrete (Chuvieco, 2016).

Figure 2: Spectral signatures for various ground objects. As seen from the graph, objects reflectance varies greatly

over various wavelengths (source: Figure modified from Chuvieco, 2016)

2.2. Image classification

Classically, the image classification process is divided into supervised and

unsupervised method. For the unsupervised method, an automatic search is performed

to find clusters of homogenous values within the image. These clusters then need to be

assigned to an information class manually by the user (Chuvieco, 2016). On the other

hand, the supervised method requires previous knowledge by the user, since the user

provides beforehand which information classes should be classified. To do so, a set of

training areas are created where each training area defines an area of pixels (or a single

pixel) representing a desired information class. The information within these training

areas are then used to classify the rest of the image (Chuvieco, 2016).

Multispectral cameras, like the name suggests, collect data for a given object in many

spectral bands. So, in contrast to normal RGB cameras which solely collect data for the

red, green and blue band of the electromagnetic spectrum, multispectral cameras

include addition bands, e.g. near infrared, short wave infrared or thermal infrared bands.

So, depending on how many bands the camera includes, the final image will consist of

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as many features. Thus, for each of the layers, each pixel value represents the

reflectance for that specific band. To classify the image, the bands that best distinguish

between suggested information classes can then be used to classify the image. For

example, to classify artificial surfaces from vegetation the near infrared (NIR) band can

be used since the reflectance for vegetation is high in the NIR band but low for artificial

surfaces.

In general, if the pixel size of an image is equal in size or coarser than the object of

interest a pixel-by-pixel classification is appropriate (Blaschke, 2010). The pixel-by-

pixel technique simply refers to that each pixel in the original image will be evaluated

and classified into a new value. However, with higher resolutions pixels can be much

smaller than the object of interest, increasing the overall complexity of the image since

single objects get split up into numerous pixels. To solve the problem where multiple

pixels make up a target object, a method named object based image analyses (OBIA)

has been utilized which merges pixels into group of pixels called segments based on

their spectral similarities (Blaschke, 2010). Subsequently, every segment will be

assigned to an information class. The OBIA segmentation method has proved to be very

useful in high resolution imagery (Franklin & Ahmed, 2017; Pande-Chhetri, Abd-

Elrahman, Liu, Morton, & Wilhelm, 2017; Puliti, Talbot, & Astrup, 2018).

Reflectance values are not the only way to separate between information classes. The

usage of texture measures, a statistical method of structure useful to separate smooth

surfaces from more rough surfaces, has proved to increase overall accuracy in very high

resolution imagery (Feng et al., 2015; Laliberte et al., 2009). Other information layers,

such as elevation and shape of objects, have been used to complement pixel or segments

separation (van der Werff & van der Meer, 2008).

2.3. Random Forest Classifier

Random Forest (RF) is an ensemble classifier which consists of multiple classification-

trees, where the results of each individual tree contributes a single vote for to which

class each pixel or segment belongs to. The most frequent class, i.e. the one with the

most votes, will be the final information class the pixel or segment will be assigned to

(Rodriguez-Galiano et al., 2012). The input variables used (often named features and

typically include image spectral bands, elevation, texture information etc.) are

randomly selected for these building trees. Furthermore, the RF algorithm has the

option to produce accuracy assessment called out-of-bag error (OOB error), which

evaluates performance by leaving out part of the training data for evaluation (Millard

& Richardson, 2015). The RF classifier is highly sensitive to selection and size of

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training areas and maximum accuracy is acquired when training areas are randomly

distributed, should be as large as possible and should have minimal spatial

autocorrelation (Millard & Richardson, 2015).

Another important factor which influences the Random Forest performance is if the

training dataset is balanced or imbalanced. If the training set is imbalanced, i.e. the

number of training pixels within each feature class is uneven, the classification may

favor the most frequent training class (Millard & Richardson, 2015). Lastly, the

Random Forest also provides features importance, i.e. what feature contributes the most

in the decision making (Pal, 2005). This can be useful for feature selection in cases

where numerous features are used.

2.4. Texture analysis and Vegetation indices

Haralick et al. (1973) wrote a paper describing fourteen texture features for image

classification. Unlike image bands, which describe spectral properties of an object,

texture describes spatial variation over a fixed area (Feng et al., 2015). Texture statistics

are derived from a gray level co-occurrence matrix (GLCM) where different angular

relationships and distance between neighboring cell pairs are calculated (Haralick et al.,

1973). Typical texture effect is differentiating between rough and smooth surfaces.

Vegetation indices (VI) are created from image bands to increase or enhance the

vegetation effects in an area (Huete et al., 2002). Therefore, for simple generalization,

it is often linked with the amount of green vegetation present on the surface. Various

VIs exist both extracted from multispectral sensors as well as RGB sensors. For

multispectral images the NDVI (Normalized Vegetation Index) is very popular, having

been used successfully in detecting healthy vegetation for numerous years (Zhou et al.,

2017, Matese et al., 2017). The VARI (Visible Atmospherically Resistance Index) is an

color index which is only utilizes the blue, green and red band from the EM ((Milas et

al., 2017). Table 1 below displays the equations for these two indices.

Table 1: Formulas for calculating the NDVI and VARI vegetation indices.

Vegetation Index Equation

NDVI (NIR–Red) / (NIR+Red)

VARI (Green-Red) / (Green+Red-Blue)

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2.5. Accuracy validation

It is necessary to validate any classified land cover map in order to have some statistical

sense of how accurate it really is. To validate the maps, first a confusion matrix is

created which compares class classified on a map to the true class on ground (usually

true samples on ground are sampled in field). Various statistical properties can be

derived from the confusion matrix such as Overall Accuracy (correctly mapped points

divided by total number of points), user´s and producer´s accuracy (chances for each

class to be correctly mapped) and Kappa, which describes how much better than chance

alone the map is (Congalton, 1991).

Another accuracy value possible to obtain is the Random Forest´s Out of bag error

(OOB error) described in section 2.3 where the classes which are not used in building

trees are used for evaluation.

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3. Methodology

To evaluate the UAV classification performance, a few subsets of tests will be

performed. Firstly, a broad image classification consisting of 5 core classes (“5-class”

classification) is performed and compared to a much more detailed classification

consisting of 11 classes (“11-class” classification). For both these instances, the same

step-by-step approach will be used and tested on both RGB and multispectral cameras.

Secondly, a more UAV focused classification will be performed by classifying a field

consisting of more than one class. In other words, the first case focuses on classical

broad land cover mapping whereas the second case really takes advantage of the UAV

high spatial resolution for examining the variation within these broadly defined classes.

3.1. Study area

For this project the agroecological field experiment plot sites (SITES) in Lönnstorp,

located in Skåne in Sweden, was chosen as the area of interest. Lönnstorp´s

environment consists of various manmade fields (Figure 3) which contain different type

of crops. Furthermore, two of the classes are mixed with old rye and legumes (clover

and lucerne). These wide range of different surface types provides a basis for

classification hierarchy, i.e. it is possible to rank easily classifiable classes from more

difficult ones. Due to the high spatial resolution of the UAV images, classifying mixed

fields (fields containing two classes) and within field variation becomes possible. At

coarser resolution, the pixel size would cause these variations to average out and thus

made harder to identify.

Figure 3: Location of Lönnstorp (yellow star) in the south of Sweden and the agricultural fields on site

containing various types of crops.

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The agricultural fields shown on Figure 3 will, among other, be used as target

information classes for the classification. Figure 4 shows a photograph taken in the field

of all these classes.

Figure 4: The agricultural fields at Lönnstorp: a) Winter Wheat (ref), b) Winter Rapeseed, c) Kernza/Lucerne, d)

Kernza, e) Winter Wheat (orig), f) Grass Legume Lay, g) Spring Barley, h) Bare soil. Photos: Author.

a) b)

c) d)

e) f)

g) h)

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3.2. Data

UAV images from 5th of May 2018 were collected in Lönnstorp using two different

camera sensors, i.e. the Parrot Sequoia multispectral sensor and GoPro 4 RGB sensor.

The Parrot Sequoia collects data in four spectral bands, as displayed in Table 2, whereas

the GoPro collects data only in the visible part of the EM spectrum.

Table 2: Available bands and their wavelengths for the Parrot Sequoia

Bands Parrot Sequoia [nm]

Blue X

Green 550

Red 660

Red Edge 735

Near Infrared 790

The UAV platform, a modified 3DR Solo quadcopter, was flown following a

predetermined flight plan at 70m altitude and collected 240 and 78 images using the

Parrot Sequoia and GoPro cameras respectively. The Agisoft PhotoScan software was

used to align and georeference the raw UAV photos and, by creating a dense point cloud

from the raw images, an orthophoto and digital elevation model (DEM) were exported

(Figure 5).

Figure 5: Orthophoto created from UAV images captured by the GoPro 4 camera (left) and multispectral image

created using the near infrared, red and green band from the Parrot Sequoia camera (right) from Lönnstorp.

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The GoPro and Parrot Sequoia camera sensors will for the rest of this thesis be called

RGB and Multispectral cameras respectively. Similarly, RGB and Multispectral image

corresponds to the images created by the GoPro and Parrot Sequoia sensors.

3.2.1. Field work – sampling method

Field work was conducted in Lönnstorp on the 11th of May 2018 where a total of 595

GPS points were collected using an RTK GPS Topcon positioning system with sub-

decimeter accuracy. These points were used as training and evaluation data for the

Random Forest classifier. Additionally, 6 ground control points (GCPs) were collected

and used to georeference the raw UAV images in Agisoft (Figure 6).

Figure 6: Location of training points (blue circles), evaluation points (red circles) and ground control points (yellow

squares) collected in the field using an RTK GPS Topcon positioning system.

In collecting the GPS points, a stratified random sampling method was applied,

meaning that fixed number of random points within each information class are created.

These points were then visited in the field and their correct information value was

confirmed. At least 2 meters separated the points and if points were located close to an

edge between two classes, those were moved to new location where they were markedly

covered by a single class.

All data (GPS points and UAV images) is harmonized to the WGS84/UTM zone 33N

coordinate reference system.

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3.3. Methods

3.3.1. Information classes – Classification strategy

To evaluate different classification strategies and compare RGB to multispectral

images, a set of tests must be performed. A total of three subtests were examined in this

project, two general cases and one specialized case (Figure 7).

Figure 7: Three different examples are used to compare RGB to multispectral image classification, i.e. two General

cases (5-class and 11-class classification) and one Specialized case.

3.3.1.1. General Case

The general classification case describes a typical classification scenario where the

surface is classified into both broad information classes (5-class classification) and

more detailed information classes (11-class classification). As the name suggests, the

broad 5-class and detailed 11-class cases describe classification scenarios consisting of

five and eleven information classes respectively (Figure 8). As seen from Figure 8, all

green vegetation surfaces listed in the 11-class classification have been merged into one

class in the 5-class case.

Figure 8: Information classes for the 11-class and 5-class classifications. All classes representing green vegetation

in the 11-class classification have been merged into a single class in the 5-class classification.

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The reason for this summarization is that the green vegetation classes all have similar

spectral signatures which differ from the other four classes (bare soil, road, rye and

rapeseed), as seen in Figure 9. Even though “Rapeseed” has similar spectral curve to

the other vegetation classes, it is classified separately due to its visual distinction. For

the rest of this thesis, the term “5-class” and “11-class” will be used to describe these

two general case classification examples.

Figure 9: Spectral signatures for the 11-class classification information classes from the multispectral camera.

After the information classes have been decided a classification strategy needs to be

formed. To compare the RGB and multispectral images, the classification process was

the same for both sensors. In this study, the first step of the analysis was to evaluate the

effect of spatial resolution by classifying each UAV image at seven different pixel sizes.

Subsequently, the UAV image generating the highest classification accuracy was

further analyzed and two ways of improving the classification tested, i.e. by applying

image segmentation and integrating additional input features to the original UAV image

bands. Furthermore, the feature importance was documented and the least important

features removed from the Random Forest input stack. The reason for removing the

least important features was to evaluate how the classification accuracy varies by

reducing input bands of less importance. Thus, the Random Forest algorithm was run a

few times until one input band, the most important one, was remaining. Since 5-class

and 11-class cases are demonstrating the same surface (only different amount of

information classes), the last two steps (segmentation and integrating additional data)

will only be performed for the 11-class case. Figure 10 displays a simple flow chart of

the steps performed.

0

10000

20000

30000

40000

50000

60000

DN

nu

mb

er

Bare Soil Grass Legume Lay Rye

Winter Wheat (orig) Winter Wheat (ref) Spring Barley

Rapeseed Kernza Kernza/Lucerne

Natural Grass Road

Green Red Red edge NIR

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Figure 10: Flow chart of the classification strategy for the 5-class and 11-class classification. Two ways of

improving the classification accuracy is tested on the 11-class classification, i.e. segmentation and integration of

additional input features.

3.3.1.2. Specialized Case

The specialized case describes a more detailed classification than the general case. The

high resolution provided by the UAV imagery allows for smaller objects to be detected

and more detailed within-field analysis. In this study, the intercropping field consisting

of Kernza (a type of wheatgrass) and Lucerne (legume) will be examined. Furthermore,

large amounts of dandelions (weed) grow within the field so total of three classes will

be classified within the field; i.e. Kernza, Lucerne and Dandelion. Since this a very

detailed classification, only the pixel-by-pixel method will be used.

3.3.2. Wavelengths and spatial resolution

As stated earlier, the Multispectral camera consists of four bands (green, red, red edge

and near infrared) and the RGB camera consists of the three visible bands of the EM.

To evaluate the effect of spatial resolution on classification accuracy, orthophotos at

seven different pixel sizes are created for both RGB and Multispectral images, i.e. at 5

cm, 10 cm, 20 cm, 30 cm, 40 cm, 50 cm and 1m. The resampling is done within the

Agisoft Photoscan software, by specifying appropriate pixel size for export based on

the dense point cloud created from the raw UAV images. Agisoft uses bilinear

interpolation in the creation process of the orthophotos (Agisoft PhotoScan User

Manual, 2018).

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3.3.3. Segmentation

For testing the impact of segmentation, an object-based image analysis (OBIA) was

used to classify groups of spectrally similar pixels instead of classifying every

individual pixel (pixel-by-pixel method) in the image. To segment the UAV images,

the TerrSet Geospatial Monitoring and Modeling Software was used. The

SEGMENTATION-module within TerrSet accepts list of layers to be used for

segmentation and by tuning a similarity tolerance the user controls the generalization

level of the segmentation. The segment algorithm groups together pixels with similar

spectral properties, where lower similarity tolerance produces smaller, more detailed

segments. Three different similarity tolerance values were used in this analysis; values

of 50, 100 and 200. These values were selected by experimentation and produce three

levels of detail segment polygons (Figure 11).

The segment polygons are exported as shapefiles from TerrSet and are used to extract

zonal statistics from the UAV images, which acts as input in the Random Forest

algorithm. Since the Random Forest algorithm needs information in tabular format to

train and classify, the UAV image is saved as a python list where each list index

contains statistical information for the image for each segment polygon. Now, the

training segments (segments which include training data points) are used to train the

classifier based on the statistical information extracted from the image within these

polygons.

Figure 11: Zoom in on an area of the multispectral image. The top left displays a false color composite of the

multispectral image and the top right, bottom left and bottom right display three levels of segmentation with

similarity tolerance set to 200, 100 and 50 respectively.

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3.3.4. Integration of additional data

Another way to improve the classification is adding other features as input to the

Random Forest algorithm. In this case, six texture features, one vegetation index (VI)

and a digital elevation model (DEM) are used as additional features.

To compute the texture features the Orfeo Toolbox, an image analysis toolbox in QGIS,

was used to calculate fourteen Haralick texture features (Haralick et al., 1973). As

suggested by Feng et al., 2015, the six least correlated texture features were chosen,

calculated from the grey level co-occurrence matrix for a single image band in four

angular directions (0°, 45°, 90°, 135°) and then taking the mean of these directions. The

texture features chosen are: correlation, entropy, inverse difference moment, mean, sum

average and variance.

The texture features are computed for a single image band. For the RGB image the red

band was chosen for texture extraction and for the multispectral image the NIR band

was chosen. These bands were chosen since they are less correlated than the other image

bands.

Another feature added as input to the Random Forest algorithm is a vegetation index.

For the multispectral image the NDVI (Normalized Vegetation Index) was chosen,

having been used successfully in detecting healthy vegetation for numerous years (Zhou

et al., 2017, Matese et al., 2017). For the RGB image, the VARI (Visible

Atmospherically Resistance Index) index was chosen. The equations for these VIs are

provided in Table 1.

Finally, a digital elevation model is exported from the Agisoft Photoscan software

which is created from the dense point cloud, generated from the raw UAV images.

3.3.5. Accuracy Assessment

To validate the classified maps, five accuracy parameters are logged; namely Overall

Accuracy (OA), Users and Producers accuracy, Kappa and the Out Of Bag score (OOB

score). The first four parameters are directly calculated from the error matrix but the

OOB score is derived from the Random Forest module.

20

3.3.6. Random Forest

All images were classified using the Random Forest classifier within the Scikit-learn

0.19.1 machine learning module in python 3.6. All parameters of the Random Forest

are the same for all classifications. A total number of 1000 trees are used, as suggested

by Millard & Richardson (2015), since using more trees does not harm the model but

rather stabilizes variable importance (Millard & Richardson, 2015). The OOB score

was logged for every classification and will act as a comparison accuracy variable to

the well-defined Overall Accuracy and Kappa value.

All the points sampled in the field are divided into training data (70%) and validation

data (30%). Since some classes have different number of points, a balanced training

sample was used where the amount of points in each class was equal to the number of

training points within the least frequent class. The feature importance was documented

during the 11-class classification to examine what features prove most valuable in the

classification process.

The size of the training data samples varies for the two cases. In the 5-class and 11-

class classification, a 25 cm buffer is created around each training point and these buffer

areas are used as training input for the classifier. The reason a 25 cm buffer is chosen

is simply to get as much variation within each class without risking overlapping into

other classes. For the specialized case (Kernza/Lucern), the training points were used

directly in the classifier, since the targeted objects are only few cm in diameter and

creating buffers in that scenario would risk averaging out the variation desired to detect.

21

4. Results

Below are the results from two test cases, i.e. the general case and specialized case

mentioned in section 3.3.1. This chapter is divided into two major sections. The first

section (4.1 General Case) contains figures and graphs from the 5-class and 11-class

classification cases whereas the second section (4.2 Specialized Case) contains results

from the specialized classification case. Only the classification yielding the highest

overall accuracy is displayed in these sections, but all output maps and confusion

matrices can be viewed in Appendix A and Appendix B respectively.

4.1. General Case

The steps followed the classification strategies mentioned in section 3.3 and the

objectives listed in section 1.1, i.e. the effect of spatial resolution, segmentation and

adding input feature were evaluated using both RGB and multispectral cameras.

4.1.1. 5-class classification

The Random Forest algorithm was tested using seven different pixel sizes and the

classification accuracy monitored. Figure 12 displays the overall accuracy curves for

the RGB and multispectral 5-class classification cases. Generally, the overall accuracy

is higher as the pixel size is smaller. The RGB camera produces the highest accuracy

of 88.3% at 10 cm pixel size and the multispectral camera produces the highest accuracy

of 86.1% at 5 cm pixel size. As seen from the graph, the RGB image accuracy generally

decreases rapidly at coarser spatial resolution. However, the multispectral image

accuracy, while also decreasing with coarser spatial resolution, decreases much less

steeply than the RGB one.

22

Figure 12: Relationship between overall accuracy and pixel size for the 5-class classification. The blue curve

represents this relationship for the RGB classification and the orange curve represents the multispectral

classification. In both cases, the overall accuracy increases as the pixel size decreases.

Figure 13 and Figure 14 display the classification maps which produced the highest

overall accuracy for the 5-class classification for the RGB and multispectral camera

respectively. Both these cases are example of pixel-by-pixel classification. As seen

from the section above, the highest accuracy was achieved having a pixel size of 10 cm

for the RGB image and pixel size of 5 cm for the multispectral one.

50

60

70

80

90

100

0 20 40 60 80 100

Ove

rall

accu

racy

[%

]

Pixel size [cm]

Overall Accuracy and Pixel Size5-class classification

RGB Multispectral

Figure 13: The 5-class classification map produced from the RGB image with 10 cm pixel size

23

Figure 14: The 5-class classification map produced from the multispectral image with 5 cm pixel size

A confusion matrix for the RGB 5-class classification is displayed in Table 3 and for

the multispectral 5-class classification in Table 4. Both tables display the user accuracy,

producer accuracy, overall accuracy, kappa value and OOB score for the classification

result. All confusion matrices for 5-class classification (both RGB and multispectral)

are displayed in Appendix B.

Table 3: Confusion matrix for the 5-class RGB classification with 10 cm pixel size. User and producer accuracies are

displayed for each information class and the final row presents the overall, kappa and OOB accuracies for the entire

classification.

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 44 1 5 0 0 0.88

Vegetation 3 82 2 7 0 0.87

Rye 0 0 10 0 1 0.91

Rapeseed 1 0 0 13 0 0.93

Road 1 0 0 0 10 0.91

Producer acc.: 0.9 0.99 0.59 0.65 0.91

Overall acc: 0.88 Kappa: 0.82 OOB: 0.88

24

Table 4: Confusion matrix for the 5-class multispectral classification with 5 cm pixel size. User and producer

accuracies are displayed for each information class and the final row presents the overall, kappa and OOB

accuracies for the entire classification.

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 46 0 0 4 0 0.92

Vegetation 7 75 3 9 0 0.8

Rye 0 0 11 0 0 1

Rapeseed 0 0 1 13 0 0.93

Road 1 0 0 0 10 0.91

Producer acc.: 0.85 1 0.73 0.5 1

Overall acc: 0.86 Kappa: 0.8 OOB: 0.92

As seen from the confusion matrices, both Rye and Rapeseed have much lower

producer accuracies then the other classes. The Rapeseed clearly gets mixed up with

the Vegetation class, which is not surprising because its spectral signature is very

similar to the other Vegetation classes (see Figure 9).

4.1.2. 11-class classification

Just like in the 5-class classification, the Random Forest algorithm was tested using

seven different pixel sizes and the classification accuracy monitored. Figure 15 displays

the overall accuracy curves for the RGB and multispectral 11-class classification cases.

Unlike in the 5-class case, here the trend is very similar for both RGB and multispectral

cameras. Again, the lowest pixel sizes generate the highest overall accuracy for both

RGB and multispectral cameras and accuracy decreases as pixel size gets larger. Both

RGB and multispectral images have the highest accuracy of 76.8% at 5 cm pixel size.

25

Figure 15: Relationship between overall accuracy and pixel size for 11-class classification. The blue curve

represents this relationship for the RGB classification and the orange curve represents the multispectral

classification. In both cases, the overall accuracy increases as the pixel size decreases.

Figure 16 and Figure 17 display the resulting classification maps for the 11-class

classification for the RGB and multispectral cameras respectively. In this section, three

classification maps for each camera sensor are created; namely pixel-by-pixel

classification (with pixel size equal to 5 cm), pixel-by-pixel with integrated additional

features and segmented classification. The segmentation classification provided the

highest accuracy when the similarity tolerance was equal to 200 for both RGB and

multispectral UAV images.

The multispectral classification map, classified using segmentation with ST equal to

200, has an overall accuracy of 94%, which is very high considering the complexity of

the surface and spectral similarities of the vegetated objects. Table 5 demonstrates the

overall accuracy, Kappa and the OOB score for the 5 cm multispectral UAV image with

and without integration of addition data, and segmentation. It is interesting that the

overall accuracy and Kappa are in similar range for all cases but the OOB score does

not follow that trend.

Table 5: Overall accuracy, Kappa and OOB score for the 11-case multispectral classification

Overall accuracy [%] Kappa [%] OOB score [%]

Image bands only 77 71 71

Additional features 88 85 99

Segmentation 94 92 64

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90 100

Ove

rall

Acc

ura

cy [

%]

Pixel Size [cm]

Overall Accuracy and Pixel Size11-class classification

RGB multispectral

26

Figure 16: Classification maps from the 11-class RGB classification: a) Result from the pixel-by-pixel method using

blue, green and red bands as input features. b) Pixel-by-pixel classification with added features (texture, VI and

DEM). c) Result from the segmented classification with similarity tolerance equal to 200.

a)

b)

c)

27

Figure 17: Classification maps from the 11-class multispectral classification: a) Result from the pixel-by-pixel

method using green, red, red edge and NIR bands as input features. b) Pixel-by-pixel classification with added

features (texture, VI and DEM). c) Result from the segmented classification with similarity tolerance equal to 200.

a)

b)

c)

28

The feature importance was also examined when adding the texture, VI and DEM

features. By analyzing the feature importance, for both RGB and Multispectral cases,

the DEM and Mean texture features score high (Figure 18). The top three important

features for the multispectral classification are the DEM, red band and Mean-texture

input features. For the RGB the Mean-texture, DEM and Sum average-texture were in

the top three list. It is also interesting to notice that all visible bands (B, G and R) are

left out after the first Random Forest iteration in the RGB classification but the red band

is the second most important one in the multispectral classification.

A confusion matrix for the RGB 11-class classification is displayed in Table 6. Due to

the table size, only the RGB table is displayed in this section. The table displays the

user accuracy, producer accuracy, overall accuracy, kappa value and OOB score for the

classification result. All confusion matrices for the 11-class classification (both RGB

and multispectral) are displayed in Appendix B. For both RGB and multispectral, the

highest accuracy was recorded in the segmentation classification with similarity

tolerance equal to 200.

Figure 18: Feature importance for 11-class classification for the RGB (left) and Multispectral (right) cases. The digital elevation model

(DEM) and the Mean texture features both prove to be important features for the classification in both cases.

29

Table 6: Confusion matrix for the RGB 11-class classification produced by segmented classification, with similarity

tolerance equal to 200. User and producer accuracies are displayed for each information class and the final row

presents the overall, kappa and OOB accuracies for the entire classification.

It is interesting to note that all classes are classified rather accurately, with both user

and producer accuracy greater than 80% for all information clasees, except for the Rye

which has the lowest producer and user accuracy of all the classes. The low producer

accuracy suggests that the chances of a randomly chosen point in the field to have the

same class on the map is low. By comparing the RGB 11-class to the RGB 5-class it is

evident that in both cases Rye gets misclassified for either Bare Soil or Grass legume

lay, which is understandable since it is only present within the Grass legume lay class.

Bare Soil

Grass Leg. Lay Rye

Winter Wheat (orig)

Winter Wheat

(ref) Spring Barlay Rape-seed Kernza

Kernza/ Lucerne

Natural Grass Road

User acc:

Bare Soil 127 0 4 2 1 5 1 0 0 0 0 0.91

Grass Leg. Lay 0 22 3 0 0 0 0 0 1 0 0 0.85

Rye 0 3 9 0 0 0 0 0 0 0 0 0.75

Winter Wheat (orig) 0 1 0 10 0 0 0 0 0 0 0 0.91

Winter Wheat (ref) 0 0 0 0 11 0 0 0 0 0 0 1.00

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.00

Rapeseed 0 0 2 0 0 0 21 0 0 0 0 0.91

Kernza 0 0 0 0 0 0 0 22 1 0 0 0.96

Kernza/Lucerne 0 0 0 0 0 0 0 0 30 0 0 1.00

Natural Grass 0 0 0 0 0 0 0 0 0 11 0 1.00

Road 0 0 0 0 0 0 0 0 0 0 11 1.00

Producer acc.: 1.00 0.85 0.50 0.83 0.92 0.83 0.95 1.00 0.94 1.00 1.00

Overall acc: 0.93 Kappa: 0.91 OOB: 0.68

30

4.2. Specialized Case

The final classification case is classifying the intercropping system Kernza/Lucerne as

well as classifying dandelion (a yellow weed) which grows wildly within that field. The

Kernza and Lucerne were sown in systematic row-wise manner as seen from Figure 19.

It is hard to visually see the row-wise pattern on the UAV images, since these two

classes are spectrally similar, but that pattern is more clearin the photograph.

The classified maps are displayed in Figure 20 and are the results of the Random Forest

pixel-by-pixel classification produced using image bands, Mean texture feature (the

most important texture feature according to section 4.1.2.2) and VI as input. As seen on

Figure 20 the multispectral camera did a much better job in classifying the Lucerne

from the Kernza. The RGB image has captured a vague row-wise pattern of the

Kernza/Lucerne system whereas the multispectral image clearly captures the row by

row division. The confusion matricies for the classification are displayed in Appendix

B.

Figure 19: Location of the Kernza/Lucern field (top left) and zoom in on the RGB and multispectral image of the

area (bottom left). To the right is a photograph displaying the row-wise pattern of the Kernza and Lucerne. The

dandelions are also visible (Photo: Author).

31

It is hard to verify the classification accuracy of the dandelions except for visual

confirmation. Figure 21 displays a zoomed-in area of the Kernza/Lucerne field and only

pixels classified as dandelions are overlaid on the map. Visually it looks good and this

distinctive yellow weed really stands out. Both vegetation indices, NVDI and VARI,

are ranked as the most important feature. Lastly, Figure 22 displays two pie charts

showing the area coverage for each class. As seen from the chart, the multispectral

classification classifies more Kernza than Lucerne which is understandable as the

Kernza stood a little higher off the ground and is therefore more likely to cover the

Lucerne at certain places.

Figure 20: Classification maps for the Kernza/Lucerne intercropping system, produced using the pixel-by-pixel

classification method with pixel size equal to 5 cm. On the left is the classification map from the RGB camera and

on the righ is the classification map from the multispectral camera. Blue is Kernza, red is Lucerne and yellow is

Dandelion.

32

Figure 21: Zoom-in area of the Kernza/Lucerne field. At top are the original RGB and multispectral images where

dandelions are clearly visible (yellow in the RGB, bright white in the multispectral). At the bottom are the same

RGB and multispectral images which are overlaid with the classified dandelions pixels (orange areas).

Figure 22: Area division for the Kernza, Lucerne and Dandelion classes for the multispectral and RGB classification

results.

33

5. Discussion

The aim of this thesis was to compare RGB and multispectral UAV imagery using the

machine learning algorithm Random Forest. Even though the subtests designed for this

project, i.e. to evaluate the effect of pixel size, texture, segmentation etc., show very

promising results, there are a great many other factors which can influence the final

output and improvements can always be made.

5.1. General Thoughts

In general, the Random Forest algorithm produced very good results and some very

interesting trends are noticeable. Firstly, the main emphasize of this project was to

compare an RGB camera to a multispectral one. By running multiple classification

examples, it is clear that an RGB camera works very well in producing general

classification maps and usually provided similar or higher overall accuracies then the

multispectral one. However, RGB cameras run into problems when spectrally similar

objects coexist at proximity, as seen from the specialized Kernza/Lucerne intercropping

map. With that being said, the multispectral camera really demonstrated the importance

of having more wavelengths bands in these kinds of scenarios. However, for general

mapping purposes an RGB camera can do the job fine.

5.2. Effects of spatial resolution, segmentation and additional features

The pixel size proves to be very important in UAV image classification. Classifying at

higher spatial resolution produces the highest accuracy maps for all cases and both

sensors. Furthermore, by integrating addition data much higher classification accuracies

can be achieved (>10% overall accuracy difference). The effects of segmenting an

image before classification and integrating additional input features also improved the

classification accuracy. Table 7 displays the highest overall accuracy, Kappa and OOB

score for every subtest of the RGB 11-class classification. By taking the sum of all the

accuracies, the additional input features to the pixel-by-pixel technique ranks the

highest. However, the OOB score is in no agreement with the other accuracy values and

thus greatly affects the outcome.

As stated before, the segmented classification produces the highest map accuracy, but

it also works well on detecting edges between classes. It also produces a speckle free

map which is not the case for pixel-by-pixel classification maps. So, for a simple

overview map of the area, the segmentation method generates the most clean and

uniform maps.

34

Table 7: Accuracy comparison for every subtest of the 11-class classification. The highest overall accuracy (OA),

kappa and OOB score are displayed for each subtest, i.e. the pixel-by-pixel method with original image bands

(RGB), the pixel-by-pixel method with additional input features (RGB/VARI/Texture/DEM) and the three

segmentation with similarity tolerance equal to 50, 100 and 200 (RGB seg50, RGB seg100, RGB seg200).

RGB RGB/VARI/Texture/DEM RGB seg50 RGB seg100 RGB seg200

OA 0.77 0.89 0.85 0.89 0.93

Kappa 0.71 0.87 0.82 0.87 0.91

OOB 0.73 0.99 0.86 0.84 0.68

Total 2.21 2.75 2.53 2.6 2.52

5.3. UAV pre- and postprocessing

The first problem regarding this UAV classification experiment is that only one image

from the phenology cycle is used for analysis. It is hard to estimate how better or worse

it will be to classify classes at different crop stages. Therefore, it would have been

interesting to compare these results with UAV images acquired later in the summer to

see if any classes dramatically increase or decrease in accuracy.

In this thesis the classification was solely based on the raw digital number (DN) values,

i.e. no correction is made and the typical conversion from DN values to reflectance was

not performed in this study. This also means that these results cannot be directly

compared to images from other dates since rainfall, atmospheric- and sun condition etc.

all influence the DN value creation process. However, for this thesis purpose,

classifying using the DN values is fine since no comparison between flights is made.

Another factor which could affect the accuracy is the fact that Agisoft Photoscan uses

bilinear interpolation in creating the orthophotos, which are then used for classifying

the surface. However, the bilinear process interpolates the true spectral value, thus

producing a DN value which is not necessarily correct. Since the scope of this effect is

not known, it would be very interesting to classify the raw UAV images before they go

through the alignment and georeferencing phase in Agisoft and compare the results.

5.4. Accuracy evaluation

The highest ranked classified maps from all subtest have an overall accuracy greater

than 85%, which is often the threshold for what counts as a good map (Foody, 2002),

except for the RGB and multispectral pixel-by-pixel 11-class maps with overall

accuracy of just under 80%.

35

The OOB score extracted from the Random Forest algorithm does not always correlate

well with the overall accuracy (OA) or kappa values. Figure 23 displays all OA values

graphed against the OOB score for every classification. As seen from the graph, a rather

weak linear relationship exists for the OOB score and OA where it seems that for a

given Overall accuracy value the OOB score will be lower.

Figure 23: Relationship between Overall accuracy and OOB score for all classification examples

R² = 0.4917

0

0.2

0.4

0.6

0.8

1

1.2

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

OO

B s

core

Overall accuracy

Overall accuracy and OOB score

36

By examining the same relationship, grouped by each classification type, the texture

classification generates correlated high OOB score and OA. The two highest OA

classified maps, the multispectral and RGB segmented classification, have considerably

lower OOB score.

Figure 24: Relationship between Overall accuracy and OOB score, grouped by each classification type, i.e. 5-class

classification (class5), 11-class classification (class11), segmentation classification (seg), classification with

additional features (texture) and the specialized Kernza/Lucerne classification (KL).

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

OO

B s

core

Overall accuracy

Overall accuracy and OOB score

class5

class11

seg

texture

KL

37

6. Conclusion

An RGB and multispectral camera sensors were tested and compared during an UAV

image classification process, where the target information classes consisted mostly of

different agricultural crop fields. Both RGB and multispectral cameras performed well,

reaching overall accuracies greater than 90%, and the RGB in many cases the better

option. However, when a more detailed classification was performed, classifying a field

containing two spectrally similar classes, the multispectral camera outperformed the

RGB.

The pixel size has a huge impact on resulting classification accuracy, where overall

accuracy varies from e.g. 58% (1 m pixel size) to 88% (5 cm pixel size) for the RGB 5-

class classification. For both RGB and multispectral cameras, higher accuracies are

achieved at higher spatial resolutions. By integrating addition data sources in the pixel-

by-pixel classification method, overall accuracies increase by a factor of >10%, e.g.

from 77% to 88% for the 11-class multispectral classification. The Mean – texture

feature turns out to be the most important texture feature for both cameras.

The highest accuracy for both RGB and multispectral classification was achieved by

classifying groups of pixels into segments where larger segments generated higher

classification accuracies, differing by a factor of 10% for the largest segments compared

to the smallest segments classification.

So, overall the objectives were all met and successfully answered. UAV works really

well for agricultural farm mapping and is a very good monitoring tool due to its quick

deployment, ease of data collection and accurate results.

38

39

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8. Appendices

8.1. Appendix A: Classification results, images.

RGB 5-class classification. Pixel-by-pixel classification.

Figure A 1: RGB 5-class classification, pixel size: 5 cm

Figure A 2: RGB 5-class classification, pixel size: 10 cm

42

Figure A 3: RGB 5-class classification, pixel size: 20 cm

Figure A 4: RGB 5-class classification, pixel size: 30 cm

43

Figure A 5: RGB 5-class classification, pixel size: 40 cm

Figure A 6: RGB 5-class classification, pixel size: 50 cm

44

Figure A 7: RGB 5-class classification, pixel size: 100 cm

45

Multispectral 5-class classification. Pixel-by-pixel classification.

Figure A 8: Multispectral 5-class classification, pixel size: 5 cm

Figure A 9: Multispectral 5-class classification, pixel size: 10 cm

46

Figure A 10: Multispectral 5-class classification, pixel size: 20 cm

Figure A 11: Multispectral 5-class classification, pixel size: 30 cm

47

Figure A 12: Multispectral 5-class classification, pixel size: 40 cm

Figure A 13: Multispectral 5-class classification, pixel size: 50 cm

48

Figure A 14: Multispectral 5-class classification, pixel size: 100 cm

49

RGB 11-class classification. Pixel-by-pixel classification.

Figure A 15: RGB 11-class classification, pixel size: 5 cm

Figure A 16: RGB 11-class classification, pixel size: 10 cm

50

Figure A 17: RGB 11-class classification, pixel size: 20 cm

Figure A 18: RGB 11-class classification, pixel size: 30 cm

51

Figure A 19: RGB 11-class classification, pixel size: 40 cm

Figure A 20: RGB 11-class classification, pixel size: 50 cm

52

Figure A 21: RGB 11-class classification, pixel size: 100 cm

53

Multispectral 11-class classification. Pixel-by-pixel classification.

Figure A 22: Multispectral 11-class classification, pixel size: 5 cm

Figure A 23: Multispectral 11-class classification, pixel size: 10 cm

54

Figure A 24: Multispectral 11-class classification, pixel size: 20 cm

Figure A 25: Multispectral 11-class classification, pixel size: 30 cm

55

Figure A 26: Multispectral 11-class classification, pixel size: 40 cm

Figure A 27: Multispectral 11-class classification, pixel size: 50 cm

56

Figure A 28: Multispectral 11-class classification, pixel size: 100 cm

57

RGB 11-class classification, pixel size equal to 5 cm. Integration of addition data;

Texture[Corr, Entr, IDM, Mean, Sum_avg, Var], VARI, DEM

Figure A 29: RGB 11-class classification. Features: B, G, R, Texture[corr, entr, IDM, mean, sum_avg, var], VARI,

DEM

Figure A 30: RGB 11-class classification. Features: Texture [ entr, mean, sum_avg, var], VARI, DEM

58

Figure A 31: RGB 11-class classification. Features: Texture [ mean, sum_avg], DEM

Figure A 32: RGB 11-class classification. Features: Texture [ mean], DEM

59

Multispectral 11-class classification, pixel size equal to 5 cm. Integration of addition

data; Texture[Corr, Entr, IDM, Mean, Sum_avg, Var], NDVI, DEM

Figure A 33: Multispectral 11-class classification. Features: G, R, R edge, NIR, Texture [corr, entr, IDM, mean,

sum_avg, var], VARI, DEM

Figure A 34: Multispectral 11-class classification. Features: G, R, Texture [ mean, sum_avg], VARI, DEM

60

Figure A 35: Multispectral 11-class classification. Features: R, Texture [ mean], DEM

Figure A 36: Multispectral 11-class classification. Features: R, DEM

61

Figure A 37: Multispectral 11-class classification. Features: DEM

62

RGB 11-class classification, pixel size equal to 5 cm. Segmentation.

Figure A 38: RGB 11-classification, segmentation (similarity tolerance: 50)

Figure A 39: RGB 11-classification, segmentation (similarity tolerance: 100)

63

Figure A 40: RGB 11-classification, segmentation (similarity tolerance: 200)

64

Multispectral 11-class classification, pixel size equal to 5 cm. Segmentation.

Figure A 41: Multispectral 11-class classification, segmentation (similarity tolerance: 50).

Figure A 42: Multispectral 11-class classification, segmentation (similarity tolerance: 100).

65

Figure A 43: Multispectral 11-class classification, segmentation (similarity tolerance: 200).

66

RGB and Multispectral classification – Kernza, Lucerne and Dandelions (pizel size

equal to 5 cm)

Figure A 44: RGB classification; Kernza, Lucerne and Dandelion

Figure A 45: Multispectral classification; Kernza, Lucerne and Dandelion

67

8.2. Appendix B: Classification results, confusion matrices.

RGB 5-class classification. Pixel-by-pixel classification.

Table B 1:RGB 5-class classification, pixel size: 5cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 48 0 2 0 0 0.96 Vegetation 7 75 4 8 0 0.80 Rye 0 0 10 1 0 0.91 Rapeseed 0 1 1 12 0 0.86

Road 0 0 0 0 11 1

Producer acc.: 0.87 0.99 0.59 0.57 1 Overall acc: 0.87 Kappa: 0.80 OOB: 0.92

Table B 2: RGB 5-class classification, pixel size: 10cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 44 1 5 0 0 0.88 Vegetation 3 82 2 7 0 0.87 Rye 0 0 10 0 1 0.91 Rapeseed 1 0 0 13 0 0.93

Road 1 0 0 0 10 0.91

Producer acc.: 0.9 0.99 0.59 0.65 0.91

Overall acc: 0.88 Kappa: 0.82 OOB: 0.88

Table B 3: RGB 5-class classification, pixel size: 20 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 44 0 6 0 0 0.88 Vegetation 2 80 0 12 0 0.85 Rye 1 1 9 0 0 0.81 Rapeseed 0 1 0 13 0 0.93

Road 0 0 1 0 10 0.91

Producer acc.: 0.94 0.98 0.56 0.52 1

Overall acc: 0.87 Kappa: 0.80 OOB: 0.83

68

Table B 4: RGB 5-class classification, pixel size: 30 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 43 1 4 2 0 0.86 Vegetation 3 70 6 15 0 0.74 Rye 1 0 7 2 1 0.64 Rapeseed 0 3 1 10 0 0.71 Road 1 0 1 0 9 0.82

Producer acc.: 0.9 0.95 0.37 0.34 0.9

Overall acc: 0.77 Kappa: 0.67 OOB: 0.71

Table B 5: RGB 5-class classification, pixel size: 40 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 46 3 1 0 0 0.92 Vegetation 4 70 5 15 0 0.74

Rye 3 0 7 1 0 0.64 Rapeseed 0 2 2 10 0 0.71 Road 0 0 1 0 10 0.91

Producer acc.: 0.86 0.93 0.44 0.38 1

Overall acc: 0.79 Kappa: 0.70 OOB: 0.76

Table B 6: RGB 5-class classification, pixel size: 50 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 42 0 4 4 0 0.84 Vegetation 3 72 2 17 0 0.77 Rye 2 1 7 0 1 0.64 Rapeseed 0 1 1 12 0 0.86 Road 0 0 0 0 11 1

Producer acc.: 0.89 0.97 0.5 0.36 0.92

Overall acc: 0.8 Kappa: 0.71 OOB: 0.83

Table B 7: RGB 5-class classification, pixel size: 100 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 15 6 2 27 0 0.3 Vegetation 2 75 0 17 0 0.8 Rye 2 5 1 2 1 0.1 Rapeseed 1 7 0 3 3 0.21 Road 0 0 0 0 11 1

Producer acc.: 0.75 0.80 0.33 0.1 0.73

Overall acc: 0.58 Kappa: 0.38 OOB: 0.73

69

Multispectral 5-class classification. Pixel-by-pixel classification.

Table B 8: Multispectral 5-class classification. Pixel size: 5 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 46 0 0 4 0 0.92

Vegetation 7 75 3 9 0 0.78

Rye 0 0 11 0 0 1.0

Rapeseed 0 0 1 13 0 0.93

Road 1 0 0 0 10 0.91

Producer acc.: 0.85 1.0 0.73 0.5 1.0

Overall acc: 0.86 Kappa: 0.8 OOB: 0.92

Table B 9: Multispectral 5-class classification. Pixel size: 10 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 43 0 3 4 0 0.86

Vegetation 4 73 2 15 0 0.78

Rye 0 0 11 0 0 1.0

Rapeseed 1 1 0 12 0 0.86

Road 2 0 0 0 9 0.82

Producer acc.: 0.86 0.99 0.69 0.39 1.0

Overall acc: 0.82 Kappa: 0.74 OOB: 0.9

Table B 10: Multispectral 5-class classification. Pixel size: 20 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 42 0 5 3 0 0.84

Vegetation 6 75 1 12 0 0.8

Rye 1 0 9 1 0 0.81 Rapeseed 3 2 1 8 0 0.57

Road 0 0 1 0 10 0.91

Producer acc.: 0.81 0.97 0.53 0.33 1.0

Overall acc: 0.8 Kappa: 0.7 OOB: 0.88

Table B 11: Multispectral 5-class classification. Pixel size: 30 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 40 4 3 3 0 0.8

Vegetation 1 73 3 17 0 0.78

Rye 0 0 11 0 0 1 Rapeseed 1 1 0 12 0 0.86

Road 1 0 1 0 9 0.82

Producer acc.: 0.93 0.94 0.61 0.38 1.0

Overall acc: 0.81 Kappa: 0.72 OOB: 0.81

70

Table B 12: Multispectral 5-class classification. Pixel size: 40 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 40 6 3 1 0 0.8

Vegetation 4 76 0 14 0 0.81

Rye 1 2 8 0 0 0.73 Rapeseed 1 1 2 10 0 0.71

Road 0 0 1 0 10 0.91

Producer acc.: 0.87 0.89 0.57 0.4 1.0

Overall acc: 0.8 Kappa: 0.7 OOB: 0.81

Table B 13: Multispectral 5-class classification. Pixel Size: 50 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 43 2 2 3 0 0.86

Vegetation 5 70 6 13 0 0.74

Rye 3 1 7 0 0 0.64 Rapeseed 1 2 2 9 0 0.64

Road 1 0 0 0 10 0.91

Producer acc.: 0.81 0.93 0.41 0.36 1.0

Overall acc: 0.77 Kappa: 0.67 OOB: 0.75

Table B 14: Multispectral 5-class classification. Pixel size: 100 cm

Bare Soil Vegetation Rye Rapeseed Road User acc:

Bare Soil 34 1 1 14 0 0.68

Vegetation 4 75 0 14 1 0.8

Rye 2 4 3 2 0 0.27 Rapeseed 1 2 0 10 1 0.71

Road 1 1 1 0 8 0.73

Producer acc.: 0.81 0.9 0.6 0.25 0.8

Overall acc: 0.72 Kappa: 0.59 OOB: 0.73

71

RGB 11-class classification. Pixel-by-pixel classification.

Table B 15: RGB 11-class classification. Pixel size: 5 cm

Table B 16: RGB 11-class classification, pixel size: 10 cm

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 121 0 4 0 2 11 0 0 1 0 1 0.86

Grass Leg. Lay 0 14 0 1 0 0 0 2 7 2 0 0.54

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 0 0 4 1 0 0 5 1 0 0 0.36

Winter Wheat (ref) 0 0 0 0 7 0 0 0 4 0 0 0.64

Spring Barlay 1 0 0 0 0 23 1 0 0 0 0 0.92

Rapeseed 0 0 2 1 0 0 18 0 0 2 0 0.78

Kernza 0 0 0 7 0 0 1 13 1 1 0 0.57

Kernza/Lucerne 0 3 0 1 7 0 0 0 18 1 0 0.6

Natural Grass 0 2 0 0 0 0 0 1 0 8 0 0.73

Road 0 0 1 0 0 0 0 0 0 0 10 0.91

Producer acc.: 1 0.74 0.63 0.29 0.412 0.68 0.9 0.62 0.56 0.57 0.91

Overall acc: 0.77 Kappa: 0.71 OOB: 0.73

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 111 0 8 1 0 15 1 0 2 0 2 0.79

Grass Leg. Lay 0 12 0 2 1 0 0 6 4 1 0 0.46

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 0 0 5 0 0 0 4 2 0 0 0.45

Winter Wheat (ref) 0 0 0 0 9 1 0 0 1 0 0 0.82

Spring Barlay 0 0 0 0 1 24 0 0 0 0 0 0.96

Rapeseed 0 0 0 0 1 2 15 3 0 2 0 0.65

Kernza 0 1 1 9 0 0 1 9 1 1 0 0.39

Kernza/Lucerne 0 2 0 2 3 0 0 1 21 1 0 0.7

Natural Grass 0 1 0 1 0 0 0 0 2 7 0 0.64

Road 0 0 1 0 0 0 0 0 0 0 10 0.91

Producer acc.: 1.0 0.75 0.55 0.25 0.6 0.57 0.88 0.39 0.64 0.58 0.83

Overall acc: 0.73 Kappa: 0.66 OOB: 0.68

72

Table B 17: RGB 11-class classification, pixel size: 20 cm

Table B 18: RGB 11-class classification, pixel size: 30 cm

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 104 0 7 0 1 24 1 1 1 0 1 0.71

Grass Leg. Lay 0 15 0 1 2 0 0 3 3 2 0 0.5

Rye 0 0 12 0 0 0 0 0 0 0 0 0.75

Winter Wheat (orig) 0 1 1 4 0 0 1 3 1 0 0 0.36

Winter Wheat (ref) 0 0 0 1 7 0 0 0 3 0 0 0.55

Spring Barlay 0 0 0 0 1 24 0 0 0 0 0 0.88

Rapeseed 0 0 0 0 1 1 18 3 0 0 0 0.65

Kernza 0 1 2 4 1 0 2 12 1 0 0 0.48

Kernza/Lucerne 0 5 0 4 2 0 0 0 18 1 0 0.5

Natural Grass 0 2 0 0 0 0 0 0 0 9 0 0.64

Road 0 0 2 0 0 0 0 0 0 0 9 0.82

Producer acc.: 0.97 0.5 0.47 0.21 0.3 0.42 0.8 0.46 0.75 0.7 0.75

Overall acc: 0.65 Kappa: 0.57 OOB: 0.55

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 99 0 7 1 1 30 0 1 0 0 1 0.71

Grass Leg. Lay 0 13 0 2 2 0 2 4 2 1 0 0.5

Rye 0 0 9 0 0 0 0 2 0 0 1 0.75

Winter Wheat (orig) 0 1 0 4 2 0 1 2 0 1 0 0.36

Winter Wheat (ref) 0 1 0 3 6 0 0 0 1 0 0 0.55

Spring Barlay 2 0 0 0 0 22 0 1 0 0 0 0.88

Rapeseed 1 1 1 1 1 1 15 0 1 0 1 0.65

Kernza 0 2 0 7 1 0 0 11 1 1 0 0.48

Kernza/Lucerne 0 5 0 1 6 0 0 3 15 0 0 0.5

Natural Grass 0 3 0 0 1 0 0 0 0 7 0 0.64

Road 0 0 2 0 0 0 0 0 0 0 9 0.82

Producer acc.: 0.97 0.5 0.47 0.21 0.3 0.42 0.83 0.46 0.75 0.7 0.75

Overall acc: 0.65 Kappa: 0.57 OOB: 0.55

73

Table B 19: RGB 11-class classification, pixel size: 40 cm

Table B 20: RGB 11-class classification, pixel size: 50 cm

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 97 1 10 0 2 29 0 0 0 0 1 0.69

Grass Leg. Lay 0 17 0 2 0 0 0 0 5 2 0 0.65

Rye 0 0 7 1 0 0 0 0 0 0 4 0.58

Winter Wheat (orig) 0 1 0 5 0 0 1 3 1 0 0 0.45

Winter Wheat (ref) 0 1 0 0 10 0 0 0 0 0 0 0.91

Spring Barlay 2 0 0 0 0 23 0 0 0 0 0 0.92

Rapeseed 0 2 6 0 0 1 10 2 0 2 0 0.43

Kernza 0 5 0 6 1 1 1 9 0 0 0 0.39

Kernza/Lucerne 0 2 0 1 7 2 0 1 15 2 0 0.5

Natural Grass 0 1 0 0 1 0 1 1 1 6 0 0.55

Road 0 0 1 0 0 0 0 0 0 0 10 0.91

Producer acc.: 0.98 0.57 0.29 0.33 0.48 0.41 0.77 0.56 0.68 0.5 0.67

Overall acc: 0.65 Kappa: 0.57 OOB: 0.54

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 102 0 7 0 2 29 0 0 0 0 0 0.73

Grass Leg. Lay 0 13 0 1 1 0 0 2 6 3 0 0.5

Rye 1 0 8 1 0 0 0 0 0 0 2 0.67

Winter Wheat (orig) 0 1 0 5 1 0 0 4 0 0 0 0.45

Winter Wheat (ref) 0 0 0 0 10 0 0 0 1 0 0 0.91

Spring Barlay 1 0 0 0 1 23 0 0 0 0 0 0.92

Rapeseed 1 1 1 0 0 1 17 1 1 0 0 0.74

Kernza 0 2 0 7 0 0 0 13 0 1 0 0.57

Kernza/Lucerne 0 4 0 0 3 1 0 3 19 0 0 0.63

Natural Grass 0 3 0 0 0 0 0 1 0 7 0 0.64

Road 0 0 1 0 0 0 0 0 0 0 10 0.91

Producer acc.: 0.97 0.54 0.47 0.36 0.56 0.43 1.0 0.54 0.7 0.64 0.83

Overall acc: 0.7 Kappa: 0.64 OOB: 0.63

74

Table B 21: RGB 11-class classification, pixel size: 100 cm

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 33 0 24 1 0 78 1 0 1 1 1 0.24

Grass Leg. Lay 0 6 1 2 0 5 0 5 1 6 0 0.23

Rye 0 0 3 2 0 3 1 1 1 0 1 0.25

Winter Wheat (orig) 0 3 0 1 0 1 0 3 1 2 0 0.1

Winter Wheat (ref) 0 0 0 0 0 0 0 1 7 3 0 0.0

Spring Barlay 1 0 0 2 0 22 0 0 0 0 0 0.88

Rapeseed 0 0 2 1 0 5 6 0 0 6 3 0.26

Kernza 0 3 0 8 1 0 0 9 0 2 0 0.39

Kernza/Lucerne 0 5 0 0 0 3 1 5 10 6 0 0.33

Natural Grass 0 0 0 0 0 1 0 1 0 9 0 0.82

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.97 0.35 0.1 0.1 0.0 0.19 0.67 0.36 0.48 0.26 0.69

Overall acc: 0.34 Kappa: 0.27 OOB: 0.45

75

Multispectral 11-class classification. Pixel-by-pixel classification.

Table B 22: Multispectral 11-class classification, pixel size: 5 cm

Table B 23: Multispectral 11-class classification, pixel size: 10 cm

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 110 0 3 0 0 20 3 2 0 2 0 0.79

Grass Leg. Lay 0 16 0 4 0 0 0 0 4 2 0 0.62

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 1 0 4 2 2 0 1 1 0 0 0.36

Winter Wheat (ref) 0 2 0 3 5 0 0 1 0 0 0 0.45

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.0

Rapeseed 1 0 0 0 0 0 21 1 0 0 0 0.91

Kernza 0 0 0 6 0 1 0 16 0 0 0 0.7

Kernza/Lucerne 0 1 0 3 3 0 0 4 19 0 0 0.63

Natural Grass 0 0 0 0 1 0 0 1 0 9 0 0.82

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.99 0.8 0.8 0.2 0.45 0.52 0.88 0.62 0.79 0.69 1.0

Overall acc: 0.77 Kappa: 0.71 OOB: 0.71

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 112 0 2 0 0 17 4 3 0 2 0 0.8

Grass Leg. Lay 0 14 0 3 0 0 0 0 9 0 0 0.54

Rye 0 0 11 0 0 0 0 0 0 1 0 0.92

Winter Wheat (orig) 0 1 0 3 4 2 0 0 1 0 0 0.27

Winter Wheat (ref) 0 4 0 2 4 0 0 1 0 0 0 0.36

Spring Barlay 2 0 0 2 1 17 0 3 0 0 0 0.68

Rapeseed 0 0 0 1 0 2 16 3 1 0 0 0.7

Kernza 1 0 0 5 3 1 1 12 0 0 0 0.52

Kernza/Lucerne 0 1 0 3 1 0 0 3 21 1 0 0.7

Natural Grass 0 1 0 0 1 0 0 0 0 9 0 0.82

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.97 0.67 0.85 0.16 0.29 0.44 0.76 0.48 0.66 0.69 1.0

Overall acc: 0.71 Kappa: 0.64 OOB: 0.69

76

Table B 24: Multispectral 11-class classification, pixel size: 20 cm

Table B 25: Multispectral 11-class classification, pixel size: 30 cm

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 111 0 1 0 0 20 5 0 0 2 1 0.79

Grass Leg. Lay 0 13 0 0 4 0 0 2 6 1 0 0.5

Rye 0 0 11 0 0 0 0 0 1 0 0 0.92

Winter Wheat (orig) 0 2 0 3 1 1 0 2 1 1 0 0.27

Winter Wheat (ref) 0 0 0 2 9 0 0 0 0 0 0 0.82

Spring Barlay 0 0 0 2 0 18 1 4 0 0 0 0.72

Rapeseed 0 0 1 0 0 3 17 2 0 0 0 0.74

Kernza 0 0 2 2 1 3 1 13 1 0 0 0.57

Kernza/Lucerne 0 1 0 2 3 1 2 2 19 0 0 0.63

Natural Grass 0 0 0 0 0 0 0 1 0 10 0 0.91

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 1.0 0.81 0.73 0.27 0.5 0.39 0.65 0.5 0.68 0.71 0.92

Overall acc: 0.73 Kappa: 0.66 OOB: 0.63

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 100 0 3 0 0 17 16 2 0 2 0 0.71

Grass Leg. Lay 0 11 0 5 2 0 0 1 6 1 0 0.42

Rye 0 0 8 0 0 0 3 1 0 0 0 0.67

Winter Wheat (orig) 0 3 0 3 1 2 0 1 1 0 0 0.27

Winter Wheat (ref) 0 1 0 2 6 0 0 0 1 1 0 0.55

Spring Barlay 1 0 0 0 0 21 0 3 0 0 0 0.84

Rapeseed 2 0 2 0 0 2 15 2 0 0 0 0.65

Kernza 0 0 0 2 0 3 0 15 2 1 0 0.65

Kernza/Lucerne 0 2 0 2 1 0 1 7 17 0 0 0.57

Natural Grass 0 0 0 0 1 0 0 0 0 10 0 0.91

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.97 0.65 0.62 0.21 0.55 0.47 0.43 0.47 0.63 0.67 1.0

Overall acc: 0.67 Kappa: 0.6 OOB: 0.62

77

Table B 26: Multispectral 11-class classification, pixel size: 40 cm

Table B 27: Multispectral 11-class classification, pixel size: 50 cm

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 99 1 3 0 0 22 8 5 1 1 0 0.71

Grass Leg. Lay 0 12 1 3 3 0 0 0 7 0 0 0.46

Rye 0 1 7 0 0 1 1 0 1 0 1 0.58

Winter Wheat (orig) 0 0 0 2 5 1 1 0 2 0 0 0.18

Winter Wheat (ref) 0 0 0 0 7 0 0 1 2 1 0 0.64

Spring Barlay 2 0 0 1 1 21 0 0 0 0 0 0.84

Rapeseed 1 0 1 2 0 1 16 1 0 1 0 0.7

Kernza 0 0 0 3 3 4 0 11 2 0 0 0.48

Kernza/Lucerne 0 1 0 1 3 0 0 2 22 1 0 0.73

Natural Grass 0 0 0 1 1 0 0 0 1 8 0 0.73

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.97 0.8 0.58 0.15 0.30 0.42 0.62 0.55 0.58 0.67 0.92

Overall acc: 0.67 Kappa: 0.6 OOB: 0.58

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 98 1 4 1 0 35 1 0 0 0 0 0.7

Grass Leg. Lay 0 15 0 3 7 0 0 0 0 1 0 0.58

Rye 0 1 9 0 0 0 1 0 0 1 0 0.75

Winter Wheat (orig) 0 1 0 4 2 1 0 2 1 0 0 0.36

Winter Wheat (ref) 0 0 0 3 8 0 0 0 0 0 0 0.73

Spring Barlay 3 0 0 1 0 19 0 2 0 0 0 0.76

Rapeseed 1 0 3 1 0 0 16 2 0 0 0 0.7

Kernza 2 0 1 3 3 3 1 9 1 0 0 0.39

Kernza/Lucerne 0 2 1 4 2 1 0 3 17 0 0 0.57

Natural Grass 0 0 0 0 1 0 0 1 1 8 0 0.73

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.94 0.75 0.5 0.2 0.35 0.32 0.84 0.47 0.85 0.8 1.0

Overall acc: 0.66 Kappa: 0.59 OOB: 0.49

78

Table B 28: Multispectral 11-class classification, pixel size: 100 cm

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 59 0 13 0 0 41 25 1 0 1 0 0.42

Grass Leg. Lay 0 9 0 1 0 0 1 1 7 7 0 0.35

Rye 0 1 3 0 0 1 2 1 1 3 0 0.25

Winter Wheat (orig) 0 3 0 0 0 0 0 6 1 1 0 0.0

Winter Wheat (ref) 0 2 0 2 0 0 0 1 0 6 0 0.0

Spring Barlay 1 0 0 0 0 17 1 4 2 0 0 0.68

Rapeseed 2 0 2 0 0 2 7 9 0 0 1 0.3

Kernza 0 1 0 1 0 9 0 8 4 0 0 0.35

Kernza/Lucerne 0 4 0 3 0 1 2 4 12 3 1 0.4

Natural Grass 0 0 1 1 0 0 0 2 0 7 0 0.64

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.95 0.45 0.16 0.0 0 0.24 0.18 0.22 0.44 0.25 0.85

Overall acc: 0.41 Kappa: 0.32 OOB: 0.26

79

RGB 11-class classification, pixel size equal to 5 cm. Pixel-by-pixel classification

including Texture (Correlation, Entropy, IDM, Mean, Sum_average, Variance), NDVI

and DEM.

Table B 29: RGB 11-class classification, pixel size: 5 cm. Features: B, G, R, Texture [Corr, Ent, IDM, Mean, Sum_Avg,

Var], VARI, DEM

Table B 30: RGB 11-class classification, pixel size: 5 cm. Features: Texture [ Ent, Mean, Sum_Avg, Var], VARI, DEM

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 119 3 6 0 0 12 0 0 0 0 0 0.85

Grass Leg. Lay 0 26 0 0 0 0 0 0 0 0 0 1.0

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 0 0 8 0 0 0 0 0 3 0 0.73

Winter Wheat (ref) 0 1 0 0 10 0 0 0 0 0 0 0.91

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.0

Rapeseed 1 0 0 0 0 0 22 0 0 0 0 0.96

Kernza 0 0 0 4 0 0 0 19 0 0 0 0.83

Kernza/Lucerne 0 0 0 0 0 0 0 2 28 0 0 0.93

Natural Grass 0 2 0 0 0 0 0 0 0 9 0 0.82

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.99 0.81 0.67 0.67 1.0 0.68 1.0 0.9 1.0 0.75 1.0

Overall acc: 0.89 Kappa: 0.87 OOB: 0.99

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 119 3 7 0 0 11 0 0 0 0 0 0.85

Grass Leg. Lay 0 25 0 1 0 0 0 0 0 0 0 0.96

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 0 0 8 0 0 0 0 0 3 0 0.73

Winter Wheat (ref) 0 1 0 0 10 0 0 0 0 0 0 0.91

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.0

Rapeseed 0 0 0 0 0 0 23 0 0 0 0 1.0

Kernza 0 0 0 4 0 0 0 19 0 0 0 0.83

Kernza/Lucerne 0 0 0 0 0 0 0 2 28 0 0 0.93

Natural Grass 0 2 0 0 0 0 0 0 0 9 0 0.82

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 1.0 0.81 0.63 0.62 1.0 0.69 1.0 0.9 1.0 0.75 1.0

Overall acc: 0.89 Kappa: 0.87 OOB: 0.99

80

Table B 31: RGB 11-class classification, pixel size: 5 cm. Features: Texture [ Mean, Sum_Avg,], DEM

Table B 32: RGB 11-class classification, pixel size: 5 cm. Features: Texture [ Mean,], DEM

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 114 2 12 1 0 11 0 0 0 0 0 0.81

Grass Leg. Lay 0 23 0 3 0 0 0 0 0 0 0 0.88

Rye 1 0 11 0 0 0 0 0 0 0 0 0.92

Winter Wheat (orig) 0 0 1 8 0 0 0 0 0 2 0 0.73

Winter Wheat (ref) 0 2 0 0 8 0 0 0 0 1 0 0.73

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.0

Rapeseed 2 0 0 0 0 0 21 0 0 0 0 0.91

Kernza 0 0 0 2 0 0 0 21 0 0 0 0.91

Kernza/Lucerne 0 0 0 0 0 0 0 3 27 0 0 0.9

Natural Grass 0 2 0 0 1 0 0 0 0 8 0 0.73

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.97 0.79 0.46 0.57 0.89 0.69 1.0 0.88 1.0 0.73 1.0

Overall acc: 0.86 Kappa: 0.82 OOB: 0.98

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 105 2 25 1 0 5 2 0 0 0 0 0.75

Grass Leg. Lay 0 20 0 3 3 0 0 0 0 0 0 0.77

Rye 1 0 11 0 0 0 0 0 0 0 0 0.92

Winter Wheat (orig) 0 0 0 7 0 1 0 0 0 3 0 0.64

Winter Wheat (ref) 0 2 0 0 8 0 0 0 0 1 0 0.73

Spring Barlay 0 2 0 0 0 23 0 0 0 0 0 0.92

Rapeseed 1 0 0 0 0 0 22 0 0 0 0 0.96

Kernza 0 0 0 2 0 0 3 17 1 0 0 0.74

Kernza/Lucerne 0 0 0 0 0 0 0 4 26 0 0 0.87

Natural Grass 0 1 0 0 3 0 0 0 0 7 0 0.64

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.98 0.74 0.31 0.54 0.57 0.79 0.81 0.81 0.96 0.64 1.0

Overall acc: 0.8 Kappa: 0.75 OOB: 0.96

81

Multispectral 11-class classification, pixel size equal to 5 cm. Pixel-by-pixel

classification including Texture (Correlation, Entropy, IDM, Mean, Sum_average,

Variance), NDVI and DEM.

Table B 33: Multispectral 11-class classification, pixel size: 5 cm. Features: G, R, R edge, NIR, Texture [Corr, Ent, IDM,

Mean, Sum_Avg, Var], NDVI, DEM

Table B 34: Multispectral 11-class classification, pixel size: 5 cm. Features: G, R, Texture [ Mean, Sum_Avgr], NDVI,

DEM

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 119 0 3 0 0 16 0 0 0 2 0 0.85

Grass Leg. Lay 0 22 0 1 2 0 0 0 0 1 0 0.85

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 0 0 8 1 2 0 0 0 0 0 0.73

Winter Wheat (ref) 0 3 0 1 7 0 0 0 0 0 0 0.64

Spring Barlay 1 0 0 0 0 24 0 0 0 0 0 0.96

Rapeseed 0 0 0 0 0 0 23 0 0 0 0 1.0

Kernza 0 0 0 0 0 0 0 23 0 0 0 1.0

Kernza/Lucerne 0 0 0 0 0 0 1 2 27 0 0 0.9

Natural Grass 0 1 0 0 1 0 0 0 0 9 0 0.82

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.99 0.85 0.8 0.8 0.64 0.57 0.96 0.92 1.0 0.75 1.0

Overall acc: 0.88 Kappa: 0.85 OOB: 0.99

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 118 0 2 0 0 17 1 0 0 2 0 0.84

Grass Leg. Lay 0 20 0 2 2 0 0 0 0 2 0 0.77

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 0 0 8 1 2 0 0 0 0 0 0.73

Winter Wheat (ref) 0 0 0 1 9 0 0 0 0 1 0 0.82

Spring Barlay 1 0 0 0 0 24 0 0 0 0 0 0.96

Rapeseed 0 0 0 0 0 0 22 1 0 0 0 0.96

Kernza 0 0 0 0 0 0 0 23 0 0 0 1.0

Kernza/Lucerne 0 0 0 0 0 0 3 2 25 0 0 0.83

Natural Grass 0 1 0 0 1 0 0 0 0 9 0 0.82

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.99 0.95 0.86 0.73 0.69 0.56 0.85 0.88 1.0 0.64 1.0

Overall acc: 0.87 Kappa: 0.84 OOB: 0.99

82

Table B 35: Multispectral 11-class classification, pixel size: 5 cm. Features: R, Texture [ Mean], DEM

Table B 36: Multispectral 11-class classification, pixel size: 5 cm. Features: R, DEM

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 117 0 2 0 0 18 1 0 0 2 0 0.84

Grass Leg. Lay 0 17 0 4 2 0 0 0 0 3 0 0.65

Rye 0 1 11 0 0 0 0 0 0 0 0 0.92

Winter Wheat (orig) 0 0 0 7 3 1 0 0 0 0 0 0.64

Winter Wheat (ref) 0 0 0 2 8 0 0 0 0 1 0 0.73

Spring Barlay 1 0 0 0 0 24 0 0 0 0 0 0.96

Rapeseed 0 0 0 0 0 0 20 2 1 0 0 0.87

Kernza 0 0 0 0 0 0 1 21 0 1 0 0.91

Kernza/Lucerne 0 0 0 0 0 0 2 3 25 0 0 0.83

Natural Grass 0 2 0 1 1 0 0 0 0 7 0 0.64

Road 0 0 0 0 0 0 0 0 1 0 10 0.91

Producer acc.: 0.99 0.85 0.85 0.5 0.57 0.56 0.83 0.81 0.93 0.5 1.0

Overall acc: 0.83 Kappa: 0.73 OOB: 0.97

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 61 1 26 1 0 40 2 6 0 3 0 0.44

Grass Leg. Lay 0 17 0 3 3 0 0 0 0 3 0 0.65

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 0 0 7 3 0 0 0 0 1 0 0.64

Winter Wheat (ref) 0 4 0 0 6 0 0 0 0 1 0 0.55

Spring Barlay 1 3 1 1 0 19 0 0 0 0 0 0.76

Rapeseed 0 0 0 0 0 0 20 3 0 0 0 0.87

Kernza 0 0 0 0 0 0 1 17 1 4 0 0.74

Kernza/Lucerne 0 0 0 0 0 0 2 3 24 1 0 0.8

Natural Grass 0 2 0 1 1 0 0 0 0 7 0 0.64

Road 0 0 0 0 0 0 2 0 0 0 9 0.82

Producer acc.: 0.98 0.63 0.31 0.54 0.46 0.32 0.74 0.59 0.96 0.35 1.0

Overall acc: 0.62 Kappa: 0.56 OOB: 0.90

83

Table B 37: Multispectral 11-class classification, pixel size: 5 cm. Features: DEM

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 31 11 6 23 20 29 2 7 0 11 0 0.22

Grass Leg. Lay 0 8 5 3 3 5 0 0 0 2 0 0.31

Rye 0 6 4 0 1 1 0 0 0 0 0 0.33

Winter Wheat (orig) 1 1 0 6 1 1 0 0 0 1 0 0.55

Winter Wheat (ref) 1 1 1 1 3 4 0 0 0 0 0 0.27

Spring Barlay 5 2 1 7 3 6 0 0 0 1 0 0.24

Rapeseed 0 0 0 0 0 0 11 3 4 0 5 0.48

Kernza 0 0 0 0 0 0 0 17 1 5 0 0.74

Kernza/Lucerne 0 0 0 0 0 0 6 2 13 2 7 0.43

Natural Grass 0 1 3 2 0 1 0 2 1 1 0 0.1

Road 0 0 0 0 0 0 7 0 1 0 3 0.27

Producer acc.: 0.82 0.27 0.2 0.14 0.1 0.13 0.42 0.55 0.65 0.04 0.2

Overall acc: 0.32 Kappa: 0.24 OOB: 0.62

84

RGB 11-class classification, segmentation.

Table B 38: RGB 11-class classification, segmentation classification [Similarity tolerance: 50]

Table B 39: RGB 11-class classification, segmentation classification [Similarity tolerance: 100]

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 112 0 5 3 0 19 0 0 0 1 0 0.8

Grass Leg. Lay 0 19 0 0 0 0 0 3 3 1 0 0.73

Rye 0 0 12 0 0 0 0 0 0 0 0 1.0

Winter Wheat (orig) 0 0 0 10 1 0 0 0 0 0 0 0.91

Winter Wheat (ref) 0 0 0 1 10 0 0 0 0 0 0 0.91

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.0

Rapeseed 0 0 0 0 0 0 23 0 0 0 0 1.0

Kernza 0 4 0 1 1 0 0 17 0 0 0 0.74

Kernza/Lucerne 0 1 0 0 0 0 0 0 29 0 0 0.97

Natural Grass 0 0 0 1 1 0 0 0 0 9 0 0.82

Road 0 0 1 0 0 0 0 0 0 0 10 0.91

Producer acc.: 1.0 0.79 0.67 0.63 0.77 0.57 1.0 0.85 0.90625 0.82 1.0

Overall acc: 0.85 Kappa: 0.82 OOB: 0.86

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 124 0 6 1 0 7 0 0 0 2 0 0.89

Grass Leg. Lay 0 24 0 0 0 0 0 2 0 0 0 0.92

Rye 0 1 11 0 0 0 0 0 0 0 0 0.92

Winter Wheat (orig) 0 0 0 8 1 2 0 0 0 0 0 0.73

Winter Wheat (ref) 0 0 0 0 11 0 0 0 0 0 0 1.0

Spring Barlay 2 0 0 0 0 23 0 0 0 0 0 0.92

Rapeseed 0 0 3 0 0 0 20 0 0 0 0 0.87

Kernza 0 4 0 0 0 0 0 19 0 0 0 0.83

Kernza/Lucerne 0 2 0 0 0 0 0 0 28 0 0 0.93

Natural Grass 0 0 0 1 0 0 0 0 0 10 0 0.91

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.98 0.77 0.55 0.8 0.92 0.72 1.0 0.9 1.0 0.83 1.0

Overall acc: 0.89 Kappa: 0.87 OOB: 0.84

85

Table B 40: RGB 11-class classification, segmentation classification [Similarity tolerance: 200]

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 127 0 4 2 1 5 1 0 0 0 0 0.91

Grass Leg. Lay 0 22 3 0 0 0 0 0 1 0 0 0.85

Rye 0 3 9 0 0 0 0 0 0 0 0 0.75

Winter Wheat (orig) 0 1 0 10 0 0 0 0 0 0 0 0.91

Winter Wheat (ref) 0 0 0 0 11 0 0 0 0 0 0 1.0

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.0

Rapeseed 0 0 2 0 0 0 21 0 0 0 0 0.91

Kernza 0 0 0 0 0 0 0 22 1 0 0 0.96

Kernza/Lucerne 0 0 0 0 0 0 0 0 30 0 0 1.0

Natural Grass 0 0 0 0 0 0 0 0 0 11 0 1.0

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 1.0 0.85 0.5 0.83 0.92 0.83 0.95 1.0 0.94 1.0 1.0

Overall acc: 0.93 Kappa: 0.91 OOB: 0.68

86

Multispectral 11-class classification, segmentation.

Table B 41: Multispectral 11-class classification, segmentation classification [Similarity tolerance: 50]

Table B 42: Multispectral 11-class classification, segmentation classification [Similarity tolerance: 100]

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 121 0 3 0 1 13 0 0 0 2 0 0.86

Grass Leg. Lay 0 22 0 0 0 0 0 0 3 1 0 0.85

Rye 0 0 11 0 0 0 1 0 0 0 0 0.92

Winter Wheat (orig) 0 0 0 6 3 2 0 0 0 0 0 0.55

Winter Wheat (ref) 0 0 0 3 8 0 0 0 0 0 0 0.73

Spring Barlay 1 0 0 0 0 24 0 0 0 0 0 0.96

Rapeseed 0 0 0 0 0 0 22 1 0 0 0 0.96

Kernza 0 1 0 3 0 2 0 16 1 0 0 0.7

Kernza/Lucerne 0 1 0 1 0 0 0 6 22 0 0 0.73

Natural Grass 0 1 0 1 0 0 0 0 1 8 0 0.73

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 0.99 0.88 0.79 0.43 0.67 0.59 0.96 0.7 0.81 0.73 1.0

Overall acc: 0.84 Kappa: 0.8 OOB: 0.81

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 129 0 3 2 0 4 1 0 0 0 1 0.92

Grass Leg. Lay 0 24 0 1 0 0 0 0 1 0 0 0.92

Rye 0 1 10 0 0 0 0 0 1 0 0 0.83

Winter Wheat (orig) 0 0 0 9 1 0 0 0 1 0 0 0.82

Winter Wheat (ref) 0 0 0 1 10 0 0 0 0 0 0 0.91

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.0

Rapeseed 0 0 0 0 0 0 23 0 0 0 0 1.0

Kernza 0 0 0 0 0 0 0 21 0 2 0 0.91

Kernza/Lucerne 0 3 0 0 0 0 0 3 24 0 0 0.8

Natural Grass 0 2 0 0 1 0 0 0 0 8 0 0.73

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 1.0 0.8 0.77 0.69 0.83 0.86 0.96 0.88 0.89 0.8 0.92

Overall acc: 0.91 Kappa: 0.89 OOB: 0.84

87

Table B 43: Multispectral 11-class classification, segmentation classification [Similarity tolerance: 200]

Bare Soil

Grass Leg. Lay

Rye Winter Wheat (orig)

Winther Wheat

(ref)

Spring Barlay

Rape-seed Kernza Kernza/ Lucerne

Natural Grass

Road User acc:

Bare Soil 133 0 1 0 2 0 2 0 0 2 0 0.95

Grass Leg. Lay 0 21 4 0 0 0 0 0 0 1 0 0.81

Rye 0 4 8 0 0 0 0 0 0 0 0 0.67

Winter Wheat (orig) 0 0 0 9 0 0 0 0 0 2 0 0.82

Winter Wheat (ref) 0 0 0 0 11 0 0 0 0 0 0 1.0

Spring Barlay 0 0 0 0 0 25 0 0 0 0 0 1.0

Rapeseed 0 0 0 0 0 0 23 0 0 0 0 1.0

Kernza 0 0 0 0 0 0 0 23 0 0 0 1.0

Kernza/Lucerne 0 0 0 0 0 0 0 0 30 0 0 1.0

Natural Grass 0 2 0 0 0 0 0 0 0 9 0 0.82

Road 0 0 0 0 0 0 0 0 0 0 11 1.0

Producer acc.: 1.0 0.78 0.62 1.0 0.85 1.0 0.92 1.0 1.0 0.64 1.0

Overall acc: 0.94 Kappa: 0.92 OOB: 0.69

88

Kernza – Lucerne – Dandelion classification. Pixel-by-pixel classification.

Table B 44: Kernza/Lucerne multispectral classification (pixel-by-pixel classification)

Kernza Lucerne Dandelion User acc:

Kernza 7 2 0 0.78

Lucerne 4 3 0 0.43

Dandelion 1 0 7 0.88

Producer acc.: 0.58 0.6 1.0 Overall acc: 0.71 Kappa: 0.55

OOB: 0.55

Table B 45: Kernza/Lucerne RGB classification (pixel-by-pixel classification)

Kernza Lucerne Dandelion User acc:

Kernza 7 2 0 0.78

Lucerne 4 3 0 0.43

Dandelion 0 0 8 1.0

Producer acc.: 0.64 0.6 1.0 Overall acc: 0.75 Kappa: 0.62

OOB: 0.92