segmentation idrisi focus paper

7/31/2019 Segmentation IDRISI Focus Paper

http://slidepdf.com/reader/full/segmentation-idrisi-focus-paper 1/2

Unlike traditional pixel-based classication methods, segment-based

classication is an approach that classies a remotely-sensed image

based on image segments. Segmentation is the process o dening

homogeneous pixels into these spectrally similar image segments.

Te goal o the segmentation process is to change the characteristics o 

the image into more meaningul ones, thus acilitating interpretation and

classication. Because these image segments better represent objects in

the landscape than do the original pixels, each step o the classication

process, rom dening training sites to classiying rom these segments,

is simplied. It is also possible to achieve better accuracy. Te common

salt-and-pepper eect that results rom a pixel-based classication is

reduced and a more cartographic-grade map is the result.

Segment-based classication is highly suited or applications that

utilize medium to high resolution satellite imagery and is a useul

addition or those mapping land cover and monitoring land change.

Other applications such as biodiversity and habitat mapping, where a

lone pixel in the classication result may not t in its context, can also

take advantage o this classication approach. Tis paper explores how

this unctionality is incorporated within IDRISI and also outlines the

workfow.

Utilizing a powerul set o existing classication tools, rom the

traditional maximum likelihood to the cutting edge machine learning

tools such as the multi-layer perceptron and classication tree analysis,

IDRISI’s methodology combines pixel-based and segment-based

approaches. Tree modules have been developed to acilitate the creation

o a segment-based classied map. SEGMENAION creates an image o

segments. SEGRAIN interactively develops training sites and signatures

SEGCLASS classies the image utilizing a majority rule algorithm.

image segmentation

Te SEGMENAION module generates an image o segments where

pixels identied within a segment share a homogeneous spectral

similarity. Across space and over all input bands, a moving window

assesses this similarity and segments are dened according to a user-

specied similarity threshold. Te smaller the threshold, the more

homogeneous the segments. A larger threshold will cause a more

heterogeneous and generalized segmentation result.

IDRISI utilizes a watershed delineation approach or the initial partition

o the input images. Ten, similar segments are merged to orm

larger segments, based on a user-specied similarity threshold. Te

segmentation process consists o three procedures:

1. Derive surace image

First, or each input image, a corresponding variance image is created using a user-dened lter.

As an example, pixels that are more homogeneous

will be assigned lower variance values, while pixels

at the boundaries o homogeneous regions will be

assigned higher values.

I there is more than one input image, the nal

surace image will be a weighted average o all

 variance images rom all image layers. Te weight o

each image is specied by the user.

2. Delineate watersheds

Te pixels’ values within the variance image arethen treated like elevation values within a digital

elevation model. Pixels are grouped into one

watershed i they are within one catchment. Each

watershed is given a unique integral value as its ID.

3. Merge watersheds

Te watersheds or image segments are then merged

to orm new segments. Te process is guided by the

ollowing logic:

Segmentation and Segment-Based Classifcation

IDRISI Focus PaperCopyright 2009 Clark Labs

The SEGMENTATION module creates an image o segments that have spectral similarity across many input bands. This

example shows two levels o segmentation rom hal-meter 4-band aerial photography in the blue, green, red and near

inrared wavelengths or Woburn, Massachusetts in 2005. The image on the let uses a larger similarity threshold than

the one on the right, resulting in more generalized, less homogeneous segments. Using this threshold, the image allows

or segments that wholly contain building eatures.

7/31/2019 Segmentation IDRISI Focus Paper

http://slidepdf.com/reader/full/segmentation-idrisi-focus-paper 2/2

classification

Te last step in segment-based classication is to classiy the image

segments. Tis is done with the assistance o a reerence image. A

reerence image is an already classied image. Tis image can be createdusing the segment-based signatures or some other approach. Te

important point here is that this reerence image is used to assign the

majority class within each segment.

A majority rule algorithm is applied within each segment to determine

its class assignment. Since the base unit in a segment-based classication

is an image segment, the accuracy may improve upon the pixel-based

classication and produce a cartographic-grade classication result as

well.

Te segment-based classication approach within IDRISI is

straightorward and bypasses the complex rule-base construction

process ound in other tools. It also provides the user with a variety o 

classication choices or the creation o the reerence image. Te optionalso exists or combining the many pixel-based classiers with this

approach to create a hybrid classication procedure not ound elsewhere.

Clark Labs

Clark University

950 Main Street

Worcester, MA 01610-1477 USA

tel:+1.508.793.7526

ax: +1.508.793.8842

email: idrisi@clarku.edu

www.clarklabs.org

-- Te segments must be adjacent and mutually most

similar.

-- Te dierence between the mean and standard deviation

 values o the two segments within a pair must be less than

the user-specied threshold. A fexible tuning mechanism

is included to allow one measurement to have more weight

over the other i appropriate. Te threshold, or similarity tolerance, controls the generalization level. Te larger the

 value, the ewer number o segments in the output.

training sites and signature development

Te next step in the classication process is to derive

training sites and signature classes rom the image

segments. In addition to the current signature development

tools, the SEGRAIN module creates training sites and

signature classes based on the image segments. With an

intuitive graphical interace, you can interactively select

segments o interest as sample sites or particular classes.

Once a segment is selected, all pixels in that segment areused or signature development. Tese images can then be

used as input into one o IDRISI’s many classiers.

The module SEGCLASS classifes the imagery using a majority rule algorithm to assign each segment to the

majority class rom the reerence image. SEGCLASS can improve the accuracy o a pixel-based classifcation and

produce a smoother map-like classifcation result while preserving the boundaries between the segments.

The module SEGTRAIN assigns these segments to specifc land cover types or the

development o training site data. The user interactively selects segments and assigns class

IDs and class names.