a novel framework for urban mapping from multispectral and hyperspectral data

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A novel framework for urban mappingfrom multispectral and hyperspectraldataK. Bakos a , G. Lisini b , G. Trianni c & P. Gamba aa Dipartimento di Elettronica , Universit di Pavia , Pavia , Italyb IUSS , Pavia , Italyc Joint Research Centre, ISFEREA Action , Ispra , ItalyPublished online: 02 Oct 2012.

To cite this article: K. Bakos , G. Lisini , G. Trianni & P. Gamba (2013) A novel framework for urbanmapping from multispectral and hyperspectral data, International Journal of Remote Sensing, 34:3,759-770, DOI: 10.1080/01431161.2012.714502

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International Journal of Remote SensingVol. 34, No. 3, 10 February 2013, 759770

A novel framework for urban mapping from multispectraland hyperspectral data

K. Bakosa , G. Lisinib , G. Triannic , and P. Gambaa*

aDipartimento di Elettronica, Universit di Pavia, Pavia, Italy; bIUSS, Pavia, Italy; cJoint ResearchCentre, ISFEREA Action, Ispra, Italy

(Received 25 February 2011; accepted 9 May 2011)

This work presents a first attempt to define a framework able to face the challengesrelated to an efficient exploitation of multispectral and hyperspectral sensors in urbanareas for mapping applications. The difference in spatial and spectral information inthe data sets, the spatial scale of the materials, and the objects involved in differentinstances of the urban mapping problem require semi-automatic approaches to selectthe most useful features and techniques according to the final mapping application. Theframework proposed here is based on a flexible processing chain and is complementedby some guidelines, aiming at helping practitioners to achieve a satisfying result fromdifferent kinds of data sets and for different mapping applications.

1. Introduction

The possibilities opened up by very high spatial resolution (VHR) multispectral data andvery high spectral resolution (hyperspectral) data for urban remote sensing are numerous.The availability of more and more spatial detail allows the extraction of objects, e.g. build-ings (Ehlers 2009), cars, and roads (Gautama et al. 2004), and improves the performanceof spaceborne data for cartographic applications. It also contributes to better managementof natural/anthropomorphic disasters, such as floods (Kux and Arajo 2008), earthquakes(Yamazaki, Yano, and Matsuoka 2005), hurricanes (Friedland, Levitan, and Adams 2008),and pollution (Nichol et al. 2006). Moreover, the availability of fine spectral detail helpsto characterize urban materials (Roessner et al. 2001), from asbestos (Marino, Panigada,and Busetto 2001) to asphalt (Herold and Roberts 2005), and to assess the impact ofenvironmental agents, such as heat island effects (Nichol 1996).

All these applications are very interesting to urban managers, but the information com-ing from remotely sensed data sets is usually too detailed (in both the spatial and the spectralsense) to be of immediate use to decision-makers. One of the best ways to convey infor-mation at their level is to use thematic maps, which help to identify objects and materialsof interest, as well as their spatial locations. Remote-sensing experts are thus challengedto design procedures able to extract different maps in a way that is, as much as possible,independent of the original sensor acquiring the image of the area under test.

Of course, any such procedure is highly dependent on the theme of the map, i.e. on thefeatures that the map should be able to highlight. The main reason is the different spatial

*Corresponding author. Email: [email protected]

ISSN 0143-1161 print/ISSN 1366-5901 online 2013 Taylor & Francishttp://dx.doi.org/10.1080/01431161.2012.714502http://www.tandfonline.com

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scales of the objects within a human settlement, from a few square metres for a car tohundreds for urban parks. Using hyperspectral data, another equally important reason isthe variable spectral characteristics of the materials, dependent on its age, composition,and geometrical positioning with respect to the sensor and the illuminating source (theso-called bidirectional reflectance distribution function (BRDF)) (Schiefer, Hostert, andDamm 2006). The mapping problem is therefore going to be in essence the search for amethodology which, taking into account the different spatial scales, geometrical proper-ties, and material characteristics of the elements of the urban scene, is able to classify asautomatically as possible the original data according to a given legend. It must be noted,additionally, that the class legend in urban areas often refers to land-use classes, i.e. toclasses (such as medium-size residential or recreational areas) that do not directly referto a specific cover (i.e. spectral characteristics) or object (i.e. spatial scale). The only wayto reduce uncertainties and improve the final mapping result is therefore the combined useof the spectral and spatial characteristics and the exploitation of the multiple scales andradiances at more wavelengths, when these information are available. A combination withsuitable a priori knowledge about the elements of the scene is a plus for the approach andallows definition of more efficient techniques and more targeted approaches, but must beconsistent with the legend of the final mapping product.

This article is devoted to analysis of a few different approaches developed formultispectral and hyperspectral data classification in urban areas using both spectral andthe spatial information. As discussed in Gamba, DellAcqua, and Ferrari (2003), there areadvantages in considering the context in addition to the very fine spectral characteriza-tion of each pixel. The disadvantages are, however, the increased number of features tobe considered and the subsequent Hughes phenomenon, i.e. the reduction of classificationaccuracy when more features are available. This work is meant to include these ideas intoa more general framework and to provide some guidelines to properly use them.

To this aim, a comparison between available methodologies and maps with differentlegends and information coming from different sensors with different resolutions willbe used to illustrate the approach and characterize the improvements with respect to thestate-of-the-art technologies.

2. Overall approach

The overall approach used in this work to analyse high-resolution spectral and spatial dataavailable on a given urban area is structured as shown in Figure 1. Generally speaking, theprocedure is organized in three steps, but not all of them are considered for any mappingproblem or spatial scale, and thus, the figure should be considered to be the more generalway to implement the framework discussed in this article.

The processing steps involved may be grouped as follows.

Pixel-based urban mapping: a set of algorithms considering each pixel as a stand-alone entity and trying to exploit as much as possible the spectral and spatialinformation contained in the data vector corresponding to the pixel location in theurban environment. These algorithms will be differently selected according to thespectral characteristics of the remote-sensing data set(s) that are inputs to the mappingprocedure.

Context-aware urban mapping: a set of procedures to refine or include in the mappingresults the information coming from neighbouring pixels. The driver for their choicewill be the spatial resolution of the above-mentioned remote-sensing data set(s).

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International Journal of Remote Sensing 761

Classifiercombination

Classification

Multispectraldata set

Hyperspectraldata set

Featureextraction

Finalmap

Pixel-based mapping

Context-based mapping

Shape-based mapping

Figure 1. A graphical representation of the overall framework proposed in this article.

Shape-based urban mapping: a set of techniques to include in the urban mappingprocedure some a priori available information about the elements that may be recog-nized in the urban environment and that belong to specific mapping classes. For thispart of the framework, the driver for a specific choice will be the class legend, i.e. thefinal aim of the mapping procedure.

Of course, the options available for each of these three steps are numerous. To the aim ofexplaining the framework that is proposed in this article, a small selection is proposed, asper the following list.

Pixel-based urban mapping: a supervised spectral classifier based on adaptive resonance theory map(ARTMAP) neural networks;

the most generally acclaimed best classifier for hyperspectral data sets, i.e.support vector machines (SVMs);

a hierarchical binary decision tree (HBDT) classifier based on the combinationof multiple processing chains;

a probability ensemble classifier combination. Context-aware urban mapping:

a class-aware Markov random field (MRF) adaptive classifier, which exploitsthe MRF framework to incorporate the spatial context for different classes;

a spatial pattern re-classifier based on the same ARTMAP scheme exploited forthe first step, but fed only with context information.

Shape-based urban mapping: a shape refining approach adaptive to the class and the level of regularizationrequired by the objects in the scene according to the spatial sampling of thesensor grid.

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In the following, a more precise discussion about different options is presented and thecriteria useful to achieve a consistent selection of algorithms for a given mapping problemis proposed.

2.1. Pixel-based urban mapping

First, the pixel-based analysis can and should be performed differently whether we con-sider multispectral or hyperspectral data as input. Multispectral data sets may be easilyanalysed by more conventional supervised classifiers (such as a maximum likelihood (ML)classifier), but in cases where spectral and possibly spatial features (e.g. textures) are con-sidered in addition to the original spectral information, more robust and non-parametricapproaches should be considered. One example is the neural network approach based on aFuzzy ARTMAP architecture in Gamba and Houshmand (2001) and successfully used forthe joint exploitation of very different data sets. This approach is supervised and has provedto be robust enough to be useful in many situations.

When the number of features becomes extremely large and the size of the training set issmall, the SVMs are instead usually considered to be the best choice. This is indeed the casefor hyperspectral data sets, even without the addition of other spectral or spatial features.SVMs are usually employed because of their robustness to the small size of the trainingset and their generalization properties. A disadvantage is that they are usually difficult andtake a long time to train, especially for inexperienced users, and results may be even worsewithout a proper parameter tuning and kernel choice.

It is true, however, that many research works published in the technical literature showthat no classifier is able to perform equally well for very different mapping problems, withdifferent legends, and using different (i.e. more or less abundant) training sets. For instance,hyperspectral data are usually interpreted in different research works, even referring to thesame urban area, by means of different processing chains, all of them including a featureextraction/reduction step followed by the actual classification. A combination of multiplechains is in the authors opinion a solution, because it makes the whole system able toexploit the advantages of all of the chains.

In this work, we rely on a novel methodology recently introduced in Bakos and Gamba(2011), which is able to select among multiple processing chains the one that is mostsuitable for a specific map class. More specifically, the original mapping problem is dis-assembled into an HBDT algorithm. Each class is mapped using a (potentially different)processing chain, starting from the class that is the easiest to discriminate against the otherones. Iteratively following the same scheme, the specific feature extraction/classifier pairis selected for all the classes.

The algorithm implementation requires us to design the structure of the HBDT in anautomatic way. This is done by computing the confusion matrices on a validation data set(different from both the training set and the ground truth) for all the considered processingchains. Following this computation, a rank list is compiled using ranking parameter Rx forthe xth class, by combining the geometric or arithmetic mean of the users Ux and producersPx class accuracy values:

Rx = (Ux + Px)/2 orU2x + P2x . (1)

The procedure is extremely fast, but, of course, very dependent on the training set, whichmust be statistically relevant and significant for the scene. Our experiments, shown in the

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next section, show that with this attention, which is a basic one for trained classifiers, theresults are satisfying.

Similarly, a different methodology for multiple classifier fusion, recently introduced inBakos, Gamba, and Zagajewsky (2010), is also considered in this work. The approach isbased on the use of class memberships to obtain the more suitable per-class combinationof the single processing chain results. Probabilities computed using different processingchains for each class are combined using a weight that is inversely proportional to theirrank for that class in the validation data set. The results obtained in the above-mentionedpaper and confirmed by those presented in this work show that the approach is slightlybetter than the HBDT, although more computationally expensive.

Finally, it should be noted that due to the strict relationship between the featureextraction/selection procedure and the classifier, a multiple processing chain combinationapproach does not make a lot of sense for multispectral data and is recommended onlywhen the number of bands is sufficiently large to require the first of these two steps toavoid the Hughes phenomenon. For multispectral data, the HBDT and class membershipapproaches may have too complex results and usually provide a very small and hardly sta-tistically significant advantage with respect to the other options discussed in the followingparagraphs.

2.2. Context-aware urban mapping

Following the graphic scheme in Figure 1, the framework proposed in this work includesa second part, dealing with the information in the immediate neighbourhood of each pixel,where the scale of this neighbourhood needs, of course, to be defined according to themapping problem.

A very powerful way to add the neighbourhood information to the pixel-based mappingresults is to exploit the MRF approach. Specifically, the MRF framework starts from theidea that all pixels are connected in a grid, and their assignment to one class or another isbased not only on their spectral properties but also on the spectral patterns of the neigh-bouring pixels. A pixel and its neighbours are therefore considered to be connected in anumber of different patterns (called cliques), and the classification is performed trying tominimize the errors in the classification, considering not only the Mahalanobis distancebetween the pixel and the class representatives but also the combination of class values onevery considered clique. As a result, the final map is obtained by minimizing a weighting(error) function based on a weighted sum of a spectral (U spectr) and a spatial (U sp) part,as detailed, for instance, in Gamba et al. (2007). The minimization of this functional isachieved by Iterated Conditional Mode (ICM), a suboptimal yet computationally efficientalgorithm, which allows the rapid reaching of a local minimum of the energy function.

The adaptive methodology added in Gamba et al. (2007) to the MRF framework forurban areas is based on the idea that in urban area there are many sharp boundaries, whereabrupt class changes (e.g. from a roof to a road or parking lot) separate homogeneousareas. This calls for very different scales for the cliques in both the boundary area andthe middle of the homogeneous objects. To simplify the issue, cliques can be consideredwith a neighbourhood size depending on the scale of the objects in the latter cases andtotally neglected (i.e. reduced to 1-pixel patterns) in the former one. This very simple trickimproves the classification result by reducing the misclassifications in isolated pixels withinthe objects, while allowing us to maintain the boundary as sharply as possible with respectto the spatial resolution of the data.

The approach is valuable for both multispectral and hyperspectral data, but of course theuse of hyperspectral data should be coupled with an adequate spectral classifier as input to

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the spatial part of the above-mentioned weighting function. Moreover, the boundary areasare extracted by means of a directional filter, which needs to be applied to a synthesizedpanchromatic image, or by means of a combination of each band, properly filtered, whichmakes the approach more cumbersome and so far not completely effective.

A less refined scheme for improving the spectral-based classification considering thecontext may be based on a spatial post-processing of the pixel-based classification map.To this aim, the same training set used before may be exploited to record the classificationpatterns and automatically extract rules to associate patterns with classes. Once these rulesare applied to all the pixels in the map, they are re-assigned to an output class accordingto the patterns around them and thus to the spatial context, irrespectively of the meaning(i.e. the spectral characteristics) of the class. The very simple idea proposed in Gamba andDellAcqua (2003) is to use as a pattern the vector with the percentage of pixels belonging toeach class in a given neighbourhood of each pixel. It is clear that this approach is going toreduce the salt and pepper classification noise, but it will also blur the edges among classesas a function of the neighbourhood width. For this reason, it is recommended to maintainthis width as very small and adapt it to the scale of the objects in the scene. The widthvalues must be comparable to, and preferably lower than, the mean size of the elements ofthe scene, to avoid as much as possible shape changes in the already detected objects.

2.3. Shape-based urban mapping

The last step in the proposed processing chain is aimed at a refinement of the objectsextracted from the scene according to their shape and the a priori information about thatspecific class: the algorithm is implemented as a set of rules comparing the shapes ofthe objects extracted by means of the previous steps with a set of predefined shapes (e.g.rectangles) or more general structures (e.g. right corners).

Specifically for buildings, the requirements to be regular may be implemented into twodifferent rules:

(1) if the area difference between a building shape and the best fitting rectangle is lowerthan a predetermined value, than the building is approximated by that rectangle;

(2) if the threshold is exceeded, then a more refined regularization approach based onthe definition of the two major directions of the shape, followed by a reduction to90 of the angles and the elimination of small protruding parts of the objects, isperformed (more details can be found in Gamba et al. 2007).

Similar rules may also be implemented for roads, where the usual assumption of hav-ing two parallel borders may be forced to improve the overall mapping reliability androbustness.

3. Experimental results

The approach discussed in this article has been tested extensively using different datasets in different geographical areas. A first test has been performed using high-resolutionhyperspectral data from the Airborne Visible-Infra Red Imaging Spectrometer (AVIRIS)sensor by NASA, which recorded data in 224 bands in 2008 and 2009 for the area aroundthe Moffet Federal Airfield, California, USA. The pixel spacing here is 20 m, which makesthe data set a rather coarse one for urban mapping. As a matter of fact, the urban areas ofMountain View and Sunnyvale (bottom) as well as Newark and Fremont (upper right) are

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Bare soilUrban areaWaterVegetation

(a) (b)

Figure 2. Moffet Federal Airfield (California, USA) test area: (a) false colour AVIRIS image; (b)reference map with the corresponding colour legend.

recognizable, together with water surfaces, vegetation, and bare soil. The whole 2008 flightline, kindly provided by the NASA Jet Propulsion Laboratory (JPL) AVIRIS team, is shownin Figure 2(a) in a false colour image, whereas the reference image used to evaluate themapping results is shown in Figure 2(b).

The second test was recorded instead by the airborne Reflective Optics System ImagingSpectrometer (ROSIS) sensor by DLR over the town of Pavia, Italy. This sensor was flownin the framework of the HySens project, managed by Deutschen Zentrum fr Luft- undRaumfahrt (DLR) and sponsored by the European Union within the transnational access tomajor research infrastructure in July 2003. The sample of the Pavia data set analysed in thiswork refers the city centre and, thanks to the fine spatial resolution (around 1.3 m), clearlyshows a typical scene for an Italian historical town: buildings, very close to one another andwith very similar roof materials; a small vegetation fraction, mainly pertaining to gardenswithin palaces or to small parks; and finally, mixed stone/asphalt-paved roads. The areais depicted using ROSIS data in Figure 3(a), whereas the ground map used to validate theresults is shown in Figure 3(b).

Shadow

Buildings

Roads

Vegetation

(a) (b)

Figure 3. Pavia data set: (a) true colour ROSIS image of the area analysed in this work; (b) referencemap with the corresponding colour legend.

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In both cases, the processing chain computational load depends heavily on the differentoptions. The most straightforward chain, applying ARTMAP classification and spatial re-processing as well as the final shape refinement may last 5 minutes on the Pavia data setusing a PP4 processor running at 2 GHz with 1 GB of RAM, with an even distribution ofthe time among the steps. Instead, on the same data set, the MRF-based approach and theHBDT classifier may require 1015 minutes each, and the probability ensemble classifiereven more, around 2030 minutes.

3.1. Urban mapping for the Moffett Airfield test site

The coarse resolution of the AVIRIS data does not allow us to discriminate among differ-ent land uses, but only simple land-cover classes. With such a simplified class legend, andalmost no attention to the details (as shown in the reference map used), one may expectthat pixel-based mapping followed by a simple spatial re-processing is the best solution toachieve a reasonable result. Moreover, since the mapping product is relatively simple, andthe urban area is significantly wide in the scene, a complex fusion procedure is likely tobe useless and not effective. As a matter of fact, even the visual comparison between theresults shown in Figures 4(a) and (b), depicting the results of SVM classification as opposedto HBDT combination of SVM applied after principal component analysis (PCA) and min-imum noise fraction (MNF) transforms and feature reduction (arbitrarily to 15 bands),reveals that no advantage comes from using the more complex combination procedure.A quantitative evaluation confirms that the 83.7% overall accuracy of the first result is notstatistically different from the 83.4% accuracy of the second one. Spatial re-processing ofthe first map provides instead a map Figure 4(c) with an overall accuracy of 85.8%. For thisspatial re-processing, a conveniently wide neighbourhood was considered (5 5 pixels),corresponding to 100 100 m2.

3.2. Urban mapping for the Pavia test site

For the Pavia test site, the fine spatial resolution of the data and the detailed mapping legendto be matched allow evaluation of all of the steps in the proposed framework. Specifically,

(a) (b) (c)

Figure 4. Classification maps for the Moffet Airfield test area: spectral classification results using(a) SVM; (b) the BHDT approach; (c) MRF after SVM.

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(a) (b) (c)

Figure 5. Classification maps for the Pavia test area: spectral classification results using (a) FuzzyARTMAP; (b) the BHDT approach; (c) the class membership approach for the fusion of multiplechains.

the hyperspectral data sets require a more sophisticated processing chain to extract sat-isfying results using a pixel-based approach. Therefore, Fuzzy ARTMAP, SVM, and MLclassifiers were applied to the original data set, or to the subset of features extracted usingfeature selection (FS), PCA, MNF, and independent component analysis (ICA). A total of12 processing chains were considered to build the HBDT and combine their abilities. Themaps resulting from a single processing chain (Fuzzy ARTMAP) as well as the HBDT andprobability ensemble are shown in Figure 5, and the confusion matrices for the first and lastmaps are reported in Table 1. As for the context-aware mapping, a comparison of the MRFand spatial re-processing results is available from Figures 6(a) and (b), while the injectionof a priori information about buildings leads to the map in Figure 6(c). Table 1 includesthe confusion matrices for Figures 6(a) and (c) for comparison with previous results.

The results in Figures 5 and 6 and Table 1 show that there is a clear advantage inusing the complete chain with respect to the original spectral-based classification, whichis the common way to use hyperspectral data. In the case of the spectral classifier, theoverall accuracy is lower than exploiting the spatial information using MRFs and shaperegularization. It is true, however, although this is not visible in the results shown in thisarticle, that the spatial resolution of the data set is an important parameter. As shown bythe previous test set and in Gamba et al. (2010), using data with coarser spatial resolution,the advantage coming with context information is still true, but basic re-processing is moreappropriate than the more complex MRF framework.

4. Conclusions

This article shows a general framework for the analysis of multispectral and hyperspectraldata sets with enough spatial and spectral resolution to be useful in urban environments.The availability of these data sets opens new possibilities for urbanization monitor-ing, urban area mapping, and the analysis of spatial relationship among environmentalindicators, such as land-use classes, temperature, and air pollution.

According to the results shown in the previous section, it is possible to define someguidelines for the exploitation of multispectral and hyperspectral data in urban mapping.

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Table 1. Confusion matrices for the maps in (a) Figure 5(a), (b) Figure 5(c), (c) Figure 6(b), and(d) Figure 6(c).

(a) Buildings Vegetation Shadow Roads

Buildings 38, 868 3 1565 4125Vegetation 977 2609 2106 666Shadow 1116 16 14, 046 616Roads 397 10 268 3411

Overall accuracy 83.2%

(b) Buildings Vegetation Shadow Roads

Buildings 4139 26 1876 1340Vegetation 303 2721 2097 1237Shadow 1000 16 14, 122 656Roads 360 19 303 3404


(c) Buildings Vegetation Shadow Roads



(d) Buildings Vegetation Shadow Roads



There are essentially three parameters to be considered: the spatial resolution, the spectralresolution, and the scales of the objects/land-use/land-cover classes to be obtained in thefinal mapping product.

Spectral resolution is important to determine the complexity of the spectral clas-sification step. The finer this resolution and thus the higher the number of bands,the more important is the Hughes phenomenon and the relevance of the training setsize. Accordingly, the complexity of the classifiers is larger and the procedure usuallyinvolves fusion at different levels, either pixel, or information, or decision.

Spatial resolution is also very important to select the best option for the context-aware refinement. In our experience, lower spatial resolutions tend to favour simplerapproaches (e.g. spatial re-classification) versus more complex and adaptive ones.When details are important, however, the larger flexibility of the adaptive approachescomes into play.

The scale of the objects in the scene is another crucial factor, in the sense that a scenedominated by a class with objects of a specific size will also drive the choice of the

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(a) (b) (c)

Figure 6. Classification maps for the Pavia test area: joint mapping results using (a) MRF afterFuzzy ARTMAP; (b) spatial re-processing after Fuzzy ARTMAP; (c) MRF and geometrical rulesafter Fuzzy ARTMAP.

context-aware algorithm parameters (such as the size of the neighbourhood in theMRF framework) or the threshold for the geometrical rules (like those used in thebuilding shape refinement).

AcknowledgementsThe authors gratefully acknowledge the financial support provided by the Hyperspectral ImagingNetwork (HYPER-I-NET) Marie Curie Research and Training Network, as well as the numerousdiscussions on this topic with Fabio DellAcqua and Prashanth Marpu. This work was partially fundedby the HYPER-I-NET FP6 RTN.

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a novel framework for urban mapping from multispectral and hyperspectral data

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