URBAN VEGETATION COVER EXTRACTION FROM HYPERSPECTRAL REMOTE SENSING IMAGERY AND GIS-BASED SPATIAL ANALYSIS TECHNIQUES: THE CASE OF ATHENS, GREECE

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1 Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 URBAN VEGETATION COVER EXTRACTION FROM HYPERSPECTRAL REMOTE SENSING IMAGERY AND GIS-BASED SPATIAL ANALYSIS TECHNIQUES: THE CASE OF ATHENS, GREECE IRO A. GEORGOPOULOU a D. P. KALIVAS a and GEORGE P. PETROPOULOS b a Agricultural University of Athens, Department of Soil Science and Agricultural Chemistry, Iera Odos 75, Botanikos 11855, Athens, Greece. b Institute of Geography and Earth Sciences, University of Aberystwyth, King Street, Aberystwyth, SY23 2DB, United Kingdom. kalivas@aua.gr EXTENDED ABSTRACT Information on the urban vegetation cover spatial coverage is important in sustainable urban planning and resourceful environmental management, whereas the same time it plays a very important role in human-environment interactions. The present study aims at evaluating the combined use of Hyperion hyperspectral imagery with the Support Vector Machines (SVMs) and Spectral Angle Mapper (SAM) pixel-based classifiers for discriminating different land-cover classes at a typical urban setting focusing particular in urban vegetation cover. As a case study, the city of Athens Greece was used. Evaluation of the derived land cover maps was performed on the basis of the error matrix statistics which was assisted by co-orbital higher resolution imagery available and field visits conducted in our study region. To ensure consistency and comparability of our results, the same set of training and validation points were used. Our analyses showed that SVMs outperformed SAM in terms of both overall classification and urban vegetation cover mapping accuracy. In particular, an overall accuracy of 86.53% and Kappa was reported for the SVMs results, whereas for SAM were 75.13% and respectively. The SVMs ability to identify an optimal separating hyperplane for classes separation exemplified the algorithm s ability to perform better, in comparison to SAM, at least this appears to be the case in our study. However, both techniques were influenced by the relatively coarse spatial resolution of the sensor, which resulted to misclassification cases due to spectral mixing effects. Yet, the potential of hyperspectral remote sensing for efficient and up-to-date derivation of the spatial representation of urban vegetation presence was evidenced, providing supportive results to efforts currently ongoing globally towards the development of accurate and robust techniques in mapping the spatiotemporal distribution of urban vegetation cover and dynamics from space. KEYWORDS: remote sensing, Geographical Information Systems, Hyperion, Support Vector Machines, Spectral Angle Mapper, Athens, Greece. 1. INTRODUCTION Urban vegetation in particular is one of the central infrastructural components of any urban ecosystem and it plays significant role within cities. The physical impact of vegetation in the urban environment is to affect the thermal environment, air quality and noise levels. In fact, urban vegetation provides ecological, social, health and economic benefits to a city s inhabitants. Urban areas are continuously increasing today as more than half of the Earth s population now lives in urban areas (Martine, 2007) and the estimated annual urban population growth rate of 1.78% is almost twice as fast as that of the global population (Van de Voorde etal, 2008). An important consequence of this is

2 evidently the change of land cover types from natural to anthropogenic impervious surfaces consisting of roofs, roads, parking lots, driveways, and sidewalks (Xian et al, 2005). Thus, the identification of urban areas especially in order to monitor urban vegetation, becomes an important issue in modern world. Earth Observation (EO) technology provides today a promising avenue in mapping and monitoring urban vegetation cover structures and of their changes on a local, regional and global scale. Use of remote sensing, often combined with Geographical Information Systems (GIS), has shown great potential in this direction thanks to its advantages over traditional procedures in terms of cost effectiveness and timeliness in the availability of information over larger areas. Urban areas in particular are characterised by a wide range of spectrally complicated properties across the electromagnetic region range because of the presence of numerous spectrally unique and ambiguous materials such as darkshingles and asphalt roads (Herold et al., 2003). Other factors that further complicate the analysis of urban areas leading to high within-class spectral variability include the 3- dimensional heterogeneity of urban areas and urban vegetation cover material aging (Herold et al., 2003; 2004; Herold & Roberts, 2005). In addition, some buildings and open spaces are covered by spectrally similar urban surface materials, which further hamper a clear discrimination between them. The recent advances in remote sensing radiometers technology have led to the launch of hyperspectral EO systems. These are sophisticated sensors which are able to record reflected light from land surface objects, ranging from visible to shortwave infrared parts of the electromagnetic spectrum, acquiring a vast amount of spectral information (Xu et al., 2008). Use of hyperspectral imagery has generally shown a promising potential in terms of different land surface targets identification and land cover mapping, including urban vegetation cover, have been proposed for this purpose (Walsh et al., 2008; Petropoulos et al., 2012a;b). Different algorithms have been proposed and image classification is evidently perhaps the most widely used approach in urban vegetation mapping (Thoreau et al., 2009; Franke et al., 2009). Image classification includes among others the following groups of approaches: pixel, sub-pixel and object based classification techniques (Lu & Weng, 2007). Unsupervised classifiers group pixels with similar spectral values into unique clusters according to statistically predefined criteria and re-assign the generated spectral clusters into information classes. On the other hand, supervised classifiers use samples of given identity for each land cover class, known as training sites, to classify image pixels of unknown identity. Supervised classifiers are also commonly divided into parametric and non-parametric. Pixel-based classifiers typically develop a signature by combining the spectra of all training set pixels for a given feature. Pixel-based classification algorithm may be parametric or non-parametric. In the case of urban areas, the assumption of normal spectral distribution is not valid due to the complexity of such landscape (Lu & Weng, 2007). In the above mentioned category of parametric classifiers the Maximum Likelihood algorithm is included (Paola & Schowengerdt, 1995). Nonparametric classifiers are suitable for the incorporation of non-spectral data into classification procedure. Among the most commonly used non parametric classifiers are artificial neural networks, decision tree classifiers, support vector machines and expert systems (Foody & Arora, 1997). Yet, studies performing comparative analysis of the performance of different classification approaches with satellite hyperspectral imagery such as that from Hyperion sensor for urban vegetation mapping in particular, are evidently scarce in scientific literature. Hyperion is the first spaceborne imaging spectrometer having the same orbital characteristics as the LandSat ETM+ multispectral sensor, acquiring spectral information in 242 spectral bands at the resolution of 30 meters. Hyperion, which is onboard the Earth Observer-1 (EO-1) satellite platform was launched in 2000 under NASA s New

3 Millennium Program. The sensor has two spectrometers, one in the visible and nearinfrared (VNIR) (bands 8-57, region nm) and the other in the shortwave infrared (SWIR) region (bands , region nm). In particular, an investigation of the potential use of Hyperion hyperspectral imagery combined with Support Vector Machines (SVMs) (Vapnik, 1995) and Spectral Angle Mapper (SAM) for deriving urban vegetation mapping, has so far been very limited, if not existent. The comparison of these two supervised algorithms performance may demonstrate their potential use in urban vegetation extraction applications. Vegetation is of particular interest as it presents a versatile resource of effectively managing and moderating a variety of problems associated with urbanization (Thoreau et al., 2009). In this context, the present study aims to appraise the use of SVMs and SAM in deriving information on the regional distribution of urban land cover types when combined specifically with Hyperion imagery. As a case study, the city of Athens has been selected due to its high heterogeneity in terms of urban vegetation features and structures as well as the importance of this area to urban studies as evidenced by various previous studies (e.g. Grimmond, 2010; Thermopolis ESA project). 2. STUDY SITE AND DATASETS Our study area includes the wider area of Athens, capital of Greece, situated in the prefecture of Attiki, extending from 23 o 39 to 23 o 42 Easting and from 38 o 4 to 37 o 55 Northing. The area represents an extensive urban area with very high level of construction and high population density. It is mainly occupied by structures like buildings and roads, although vegetation presence is low. The Hyperion imagery used in our study was acquired on August 27 th, The Hyperion imagery was obtained from a previous study conducted in the area (see Petropoulos et al., 2012b), in which authors had originally acquired the imagery from United States Geological Survey (USGS) archive as a full long scene in geotiff image format and already radiometrically corrected, geometrically resampled and registered to WGS84 coordinate system, with elevation correction applied. Additionally, co-orbital Google Earth imagery was used in our study to support different aspects of methodology implementation and results interpretation. 3. METHODS 3.1. Data pre-processing Image classification was conducted to the Hyperion imagery by applying the Support Vector Machine (SVM) and the Spectral Angle Mapper (SAM) classification approaches in ENVI 4.7 software platform. No pre-processing was applied to our Hyperion imagery as it was supplied already pre-processed (further details in Petropoulos et al., 2012b). Briefly, at first the Hyperion imagery was converted into ENVI format files that contain wavelength, full width half maximum and bad band information. Then the water absorption bands were eliminated to minimize the influence of atmospheric scatter and of water vapor absorption, caused by well mixed gasses. Subsequently, Minimum Noise Fraction (MNF) transformation was performed on all Hyperion bands that had not been masked out (136 in total) in ENVI as a linear transformation in order to separate the noise from data and to minimize the influence of systematic sensor noise during image analysis. Hyperion final dataset, after the implementation of an inverse MNF, consisted of 136 bands, 46 in the VNIR and 90 in the SWIR. After this step, the resulting image was reduced to a subset of the studied region. These final 136 bands were used in the present study to perform our classification using the selected classifiers.

4 3.2. Training data selection In this study, both SVMs and SAM classifiers were applied to the Hyperion imagery using training data representative of the different land cover types included in our classification key (Table 1). Selection of the training data was assisted by the co-orbital Google earth imagery and selected field visits conducted. The training sites were selected from the Hyperion imagery and were carefully delimited. As training sites were selected pixels representative of the most homogeneous areas. In total, five classes were created: asphalt and buildings which represented the impervious surface of study area and trees, low vegetation and bare soil which represented the according pervious surface. Approximately pixels per class (in total 1007 pixels) were identified as training data, representing the classes defined in the classification scheme. Subsequently, the two algorithms were implemented, using the same training sites collected. Table 3.1. The classification key used in the study area. Class Name ID Class description Asphalt 1 Surfaces covered principally with asphalt, roads Buildings 2 Urban fabric, urban area Bare soil 3 Open areas with no vegetation, rocks or previously burnt Trees 4 Surfaces principally covered with trees Low vegetation 5 Open areas with little or vegetation of low height 3.3. Hyperion classification Support Vector Machines (SVM) SVMs is a supervised non-parametric statistical learning technique. The subset of points that lie on the margin (called support vectors) are the only ones that define the hyperplane of maximum margin (Vapnik, 1995). The most important characteristic of SVMs is its ability to generalize a limited amount and/or quality of training data. This is in line with the support vector concept that relies only on a few data points to define the classifier s hyperplane. SVM can yield comparable accuracy using a much smaller training sample size, compared to other alternative methods. Furthermore, as non parametric, SVMs do not assume a known statistical distribution of the data to be classified. The binary classification scheme in SVMs can be extended to a larger number of classes N (where N>2). SVMs was implemented using the radial basis function (RBF) kernel for performing the pair-wise classification, as its use has generally shown satisfactory results (e.g. Petropoulos et al., 2012), while it requires defining a small number of parameters and it produces generally good results in most classification cases. RBF kernel performs the pair-wise classification (one against one approach) which is a technique that N(N-1)/2 SVMs are produced following a binary tree-like fashion. The input parameters required for running SVMs in ENVI software include the gamma (γ) in the kernel function, the penalty parameter, the number of pyramid levels to use and the classification probability threshold value. Regarding the parameterization RBF kernel function, the γ parameter was set to a value equal to the inverse of the number of the spectral bands of Hyperion imagery (0.007) and the penalty parameter was set to its maximum value (100) focusing at no misclassification during the training process. The pyramid parameter was set to a value of zero forcing the Hyperion imagery to be processed at full resolution, whereas a classification probability threshold of zero was used meaning that all pixels had to be classified into one class.

5 Spectral Angle Mapper (SAM) SAM is a supervised pixel based classification method that permits rapid mapping by calculating the spectral similarity between the image spectrums to reference reflectance spectra (Kruse et al., 1993). SAM measures the spectral similarity by calculating the angle between the two spectra, treating them as vectors in n-dimensional space (Rowan & Mars, 2003). Small angles between the two spectrums indicate high similarity and high angles indicate low similarity. This method is not affected by solar illumination factors, because the angle between the two vectors is independent of the vectors length. In the present study, the maximum angle (radians) selected was 0.3. The input parameters required for evaluating SAM in ENVI software include the set of maximum angle (radians). The option of single value was set, which indicates that a single threshold was used for all classes. The field of maximum angle (radians) indicates the maximum acceptable angle between the endmember spectrum vector and the pixel vector. Pixels with an angle larger than this value are not classified by the application. The default value was 0.1 but as proven the value of 0.3 attributed better results Classification accuracy assessment Accuracy assessment of the thematic maps produced from the implementation of the SVMs and SAM classification techniques to the Hyperion imagery was performed in ENVI based on the confusion matrix analysis (Congalton, 1991; Congalton & Green, 1999). It was based on the computation of the overall accuracy (OA), the user s accuracy (UA), the producer s accuracy (PA) and the Kappa statistic coefficient (Kc). OA is the ratio of the number of validation pixels that have been correctly classified to the total number of validation pixels used for all classes and is expressed as a percentage (%). Kc is the proportion of correctly classified validation points after random agreements are removed and it expresses the extent to which the matrix results are not obtained by chance or random. In comparison to OA, Kc indicates a more conservative estimation than a simple percentage value. PA expresses the probability that the classifier has correctly labeled an image pixel, whereas UA expresses the probability that a pixel belongs to a given class and the classifier has labeled the pixel correctly into the same given class. In performing the accuracy assessment herein, a total of 193 sampling points for the different classes were selected (approximately pixels per class) directly from the Hyperion imagery following a random sampling strategy, and these points formed the validation dataset. The selection of these validation points was performed following exactly the same criteria used for the selection of training points. They were selected from Hyperion imagery and the interpretation was completed with the support of an image of the same date in Google Earth application. 4. RESULTS AND DISCUSSION The urban vegetation cover maps produced after the implementation of the SVMs and SAM classifiers to the Hyperion image of the study region are illustrated below, whereas the statistical results obtained after the classification accuracy assessment conducted using the same set of validation points are presented in Table 4.1 for both classifiers respectively. Both classification methods produced comparable results in terms of describing not only the spatial distribution but also the cover density of each land cover category in the test site. it appears that SVMs generally outperformed the SAM classifier in both overall accuracy and individual classes accuracies. SVMs overall accuracy and Kappa coefficient were 86.53% and respectively, while SAM s classification overall accuracy and Kappa coefficient were 75.13% and respectively. Among the two classifiers, SVMs was more accurate than SAM in describing the spatial distribution of

6 classes, which was also indicated according to the statistics of the individual classes results. In terms of the individual classes accuracy, PA varied from 57.6% to 96.9% and UA varied from 73.3% to 90.5% for SVMs, whereas PA varied from 27.3% to 98.5% and UA ranged from 52.9% to 86.1% for SAM. On the basis of PA statistical measure, it can be observed that the class of low vegetation produced the lowest percentage in both techniques. In addition, as it can be observed the class of low vegetation performed the lowest percentage in SAM technique and the class of bare soil performed the lowest percentage in SVM technique. This can be attributed in part to the similar spectral characteristics between the two classes, which was most probably affected by the mixed pixels combined with the low spatial resolution of the Hyperion sensor. SVMs have been designed to identify an optimal separating hyperplane for classes separation, which other machine learning classifiers may not be able to locate. SVMs technique is also able to generalize this optimal separating hyperplane unseen samples with least errors among all separating hyperplanes, thus producing the best classes separation at the end of classification (Huang et al., 2002). SVMs are also successful in addressing ill-posted problems providing high classification accuracy results in comparison to other classifiers, even in cases when small training sets are used and has important advantages. As regards SAM, its main advantages are that it is an easy and rapid method for mapping the spectral similarity of image spectra to reference spectra. It is also a powerful classification method because it represses the influence of shading effects to accentuate the target reflectance characteristics (De Carvalho & Meneses, 2000). Table 4.1. Summary of the results from the classification accuracy assessment conducted. Ground truth (percent) Class SVM PA (%) SVM UA (%) SAM PA (%) SAM UA (%) Asphalt Buildings Bare Soil Trees Low vegetation Producer s accuracy (%) Kappa Coefficient

7 Figure 4.1. The Hyperion classification using SVMs RBF classifier (right image) and SAM classifier (left image). 5. CONCLUSIONS The combined use of Hyperion hyperspectral imagery with the Support Vector Machines (SVMs) and Spectral Angle Mapper (SAM) classifiers for discriminating different landcover classes at a typical urban setting was the main objective of this study. The research focused on extracting urban vegetation cover. The comparative performance of two classifiers showed that SVMs outperformed SAM in terms of both overall and individual classes accuracy, at least for the experimental setting. The higher classification accuracy reported by SVMs is mainly attributed to the fact that this classifier has been designed to identify an optimal separating hyperplane for classes separation. However the main disadvantage of SVM classification technique is that it is not able to consider the subpixel heterogeneity which is very high in urban areas like the test site of the study. Moreover, the spatial resolution of sensors such as Hyperion is very high which makes spectral mixing problematic as the Earth s surface heterogeneity increases (Xu et al., 2008). Consequently, there is a demand for further evaluation of classification algorithms and further research in techniques which perform better results in sub-pixel level and deal with the problem of heterogeneity. Spectral confusion in pixels, may potentially lead to classification errors for a spectral class, which may consist ofan important barrier in both classification approaches examined, especially when those applied in remote sensing imagery that is not of very high spatial resolution. Finally, results of this study indicated that using low cost Hyperion hyperspectral satellite imagery can potentially enhance a wider use of techniques towards managing and monitoring classification of urban

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