MULTITEMPORAL AND/OR POLARIMETRIC SAR CHARACTERIZATION OF URBAN AREAS *

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1 MULTITEMPORAL AND/OR POLARIMETRIC SAR CHARACTERIZATION OF URBAN AREAS * Fabio Dell Acqua (1), Paolo Gamba (1) (1) Department of Electronics, University of Pavia Via Ferrata, 1,I Pavia, ITALY {f.dellacqua, p.gamba}@ele.unipv.it Abstract Polarimetric SAR data sets have shown their potential for detecting built-up areas and discriminating their different types. In this paper a study is performed on the combination of polarimetric SAR data from different sensors at different bands and different times, for detection and discrimination of urban areas. The considered data set includes SAR data from ERS, RADARSAT and SIR-C/X-SAR missions, sensed over the town of Pavia, northern Italy, a location on which we have detailed, good-quality ground truth available. Keywords Polarimetric SAR data, built-up area detection, built-up area discrimination. 1 INTRODUCTION A few studies have shown that polarimetric SAR data sets may be useful for discriminating different urban environments, in addition to detecting urban areas themselves using for example fractal properties [1] or spatial features [2]. In particular, in [3] the AIRSAR data over Sydney have been considered, and the potentials for characterising different building clusters by extracting single, double and triple bounce effects have been demonstrated; in [4] a segmentation-based classification is instead proposed, which exploits a particular decomposition of the data to improve polarimetric discrimination capability in suburban areas. Similarly, in [5] the different statistical properties of built aggregates in a urban area using SIR-C measurements have been discussed in order to provide a basis for an efficient segmentation of polarimetric urban SAR data sets. Still on segmentation, in [6] it is shown that even better results can be obtained if the problem of speckle is taken care of. Finally, the availability of SRL-1, 2 and possibly SRTM mission allows discussing multitemporal and multiparametric characterisation of urban areas discussing the mutual effects of temporal/polarimetric redundancy [7]. To provide a quantitative discussion on the usefulness and the limits of SAR data sets at the current spatial resolution that satellite sensor are able to provide, and to further validate these results, a comprehensive study is proposed in this paper using ERS, RADARSAT and SIR-C/X-SAR data over the town of Pavia, northern Italy. The structure of the paper is as follows: next chapter describes the polarimetric data set, the ground truth and the methods we used for classification. Chapter 3 illustrates and comments the results obtained on the different choices of data subsets, while in Chapter 4 some conclusions are drawn. 2 DATA SET AND TOOLS The complete data set comprises nine ERS images, five SIR-C/X-SAR polarimetric sets and one RADARSAT image over the same urban area. Table 1 provides the information about all these images. Using such data set, many investigations may be produced, and results can be compared to the available ground truth. On the site of Pavia we have good ground truth maps, thanks to the precise knowledge of the site, to the easiness of ground surveys and to the availability of regional technical maps with a sufficient ground resolution (less than 1 meter). In addition to the basic classes of water, vegetation and built-up areas, more refined classifications such as three different levels of building density (city centre, residential areas, sparse buildings) are available, but may of course be ignored where unnecessary. For each case of classification we will make explicit the set of classes used for that case. The data can be investigated, using the well-tested neural classifier described in the following, for the possibility to discriminate among different urban environments (city centre vs. residential areas vs. sparse buildings) and the possibility to extract information about some of the most important land cover/land use classes (built-up areas, streets and railways, vegetation, water, ). To this aim, the neural kernel classifier used in this research is based on a two-step approach, performing first the image segmentation on a pixel-by-pixel basis. Then, the spatial analysis is carried out by means of a second classification, using as input vector the percentages of pixels in a window around the current pixel position assigned to * This work was funded by the Italian Space Agency (ASI), contract I/R/063/01.

2 each class by the first classifier. These steps are implemented either by an unsupervised neural network (ART-2), followed by a fuzzy clustering using a standard Fuzzy-C-Means (FCM) algorithm [8], or a supervised fuzzy ARTMAP structure [9]. More in particular, the fuzzy ARTMAP classifier works along the following steps: first, the input vector is normalised (in the so called input layer, F 0 ). Then, it is compared (in the comparison layer, F 1 ) with the data prototypes coming from previous inputs and stored in the memory layer, F 2. If the comparison (essentially, an inner product) provides a value larger than a fixed threshold, then a resonance has been found and the memory vector is updated taking into account the new input. If no resonance is reported, then a new memory vector is added to the F 2 layer. This new memory stores the information added to the global knowledge of the network by the unknown input. The fuzzy ARTMAP neural network is a supervised neural network defined using two fuzzy ART blocks, called "ART a ", "ART b ", where the ART dynamics are ruled using fuzzy intersection (( p q) i = min( p i,q i )) instead of the inner product we mentioned before. The two modules are connected via a "Map Field", whose role is to map input to output memories, providing a connection between the two classes. This structure was introduced to solve pattern recognition problems. For classification, there is no need for the complete ART b, and a simplified structure was proposed in [10], and investigated also in [11]. This simplified fuzzy ARTMAP exploits only one fuzzy ART network, and is used in this work. Table 1. Our data set on Pavia (not exhaustive). Acquisition date Sensor Frequency band(s) Polarization(s) 13 August 1992 ERS-1 C VV 22 October 1992 ERS-1 C VV 24 June 1993 ERS-1 C VV 11 November 1993 ERS-1 C VV 14 April 1994 SIR-C/X-SAR C, L (X) HH, HV, VV (VV) 16 April 1994 SIR-C/X-SAR C, L (X) HH, HV, VV (VV) 17 April 1994 SIR-C/X-SAR C, L (X) HH, HV, VV (VV) 18 April 1994 SIR-C/X-SAR C, L (X) HH, HV, VV (VV) 3 October 1994 ERS-1 C VV 9 November 1994 ERS-1 C VV 22 July 1995 ERS-1 C VV 23 July 1995 ERS-2 C VV 27 August 1995 ERS-1 C VV 20 October 2000 RADARSAT-1 C VV 27 October 2000 ERS-2 C VV 3 RESULTS AND DISCUSSION We compare classification results obtained by using polarimetric, multitemporal or multitemporal/polarimetric data sets, aggregating SAR polarisations or dates and exploring also the interactions among SAR data recorded by different sensors. Finally we also consider texture measures. Since SIR-C/X-SAR data were recorded in April 1994, they partially overlap with the ERS images, providing a unique set for this kind of analysis. Classifications are obtained also by exploiting texture measures for a better discrimination of urban environments. We want to discuss which are the advantages of a polarimetric data set, and if the use of more polarisations effectively overcomes the possibility to combine multitemporal single polarisation images. This could be used to determine which of the many functioning modes of the ASAR sensor would be most useful for urban mapping and/or monitoring applications. So, in the following we will provide first a discussion of the classification results using fuzzy ARTMAP on multitemporal/multipolarization/multisensor data, considered as multiband images. Then, we will offer some results on the use of texture measures for further classification of environments inside the urban area. 3.1 Multitemporal classification In this section we focus on the effect of considering different images from the same sensor (ERS-1 SAR), at the same

3 polarisation (VV, which is implied by taking images from ERS SAR), at different times on the same area. To this aim, we considered first a single-image data set, consisting of the oldest image only, performed the classification and reported the result onto the first row of Table 2. We then formed new data sets, by adding one image at a time, and repeated the procedure. Results for each single data set are reported in each row of the same table, in ascending order of number of elements in the data set. Table 2. Classification accuracy for multitemporal results. Every row adds a new image to the data set used. In the first column, the box indicates what image has been added to the others reported in previous rows to obtain the classification results reported in that row. Subset Om.Acc. C.A. (veg.) C.A. (buil.) C.A. (water) Omiss.A. (veg.) Omiss.A. (buil.) Omiss.A. (water) 13 Aug % 69.68% 58.69% 62.39% 90.89% 37.55% 10.04% +22 Oct % 91.13% 57.10% 95.05% 93.25% 56.27% 55.10% +24 Jun % 90.58% 64.24% 92.58% 94.12% 52.81% 80.42% +11 Nov % 90.85% 62.69% 93.53% 93.92% 51.88% 91.32% +3 Oct % 92.05% 60.78% 96.05% 93.77% 54.28% 96.08% +9 Nov % 92.64% 61.17% 97.01% 93.89% 56.33% 96.81% +22 Jul % 92.94% 59.78% 97.03% 93.70% 56.72% 97.73% We note that three images are enough to reach a saturation in the accuracy levels, which is acceptable for vegetation and water (both over 90%), but unsatisfactory for built-up areas (50 to 60%). The saturation is reached at the same number of images for all of the three classes. 3.2 Multipolarization classification In this subsection we focus on the use of more than one polarisation, Considering the image acquired on 16 th April 1994, the results obtained from the L-band and the C-band data are summarised in the following Table 3. Table 3. Classification accuracy for multipolarisation results. band Overall Om. Water Comm. Water Om. Veg. Comm. Veg. Om. Built-up Comm. Built-up C 85.34% 20.93% 6.79% 86.27% 96.09% 89.48% 66.98% L 82.41% 77.97% 15.94% 82.59% 96.17% 81.97% 62.85% C+L 82.51% 79.49% 14.36% 81.56% 97.43% 89.29% 65.92% The first fact to be noted is that using multipolarisation data we get a significant increase in the omission accuracy of built-up areas with respect to multitemporal data (Table 2), unfortunately accompanied by a collapse in the commission accuracy of water. The commission accuracy of built-up areas reports only a slight improvement; omission on the class vegetation decays by about 10% and commission on the same class improves by nearly the same amount. Omission accuracy of water ranges from a 21% on band C to 80% on the combination of L and C band data. From these observations it seems that, for the purpose of detecting urban areas, using multipolarisation data is generally advantageous with respect to combining multitemporal, single-polarisation images, but other classes may become unreliable. This fact, however, deserves further investigation and validation. 3.3 Multisensor classification In this subsection we describe the results obtained with a multisensor analysis, combining data coming from both missions ERS and SIR-C/X-SAR, acquired in the year Examples of results are reported in Fig. 1. Through this analysis we have tried to improve the results of the previous classifications, using the most advantageous features of each image, due to the different characteristic of the sensors; from the single classifications it turned out that: ERS images display a good behaviour in areas covered with water and vegetation, while allow only poor performances on building classification. SIR-C/X-SAR present a good classification on buildings and vegetation; areas covered with water are actually detected but with a quite low accuracy. To realise the above it was necessary to build a suitable training set, such that the single ERS and SIR-C/X-SAR images were correctly classified.

4 (a) (b) (c) (d) Fig.1: Spatial classification maps: (a) 16,17 April SIR-C images + 8 Oct. ERS-1 and 9 Nov. ERS-1 images; (b) 16,18 April SIR-C images + 8 Oct. ERS-1 and 9 Nov. ERS-1 images; (c) 17,18 April SIR-C images + 8 Oct. ERS-1 and 9 Nov. ERS-1 images; (d) 16,17,18 April SIR-C images + 8 Oct. ERS-1 and 9 Nov. ERS-1 images This procedure resulted in high values of accuracy; the classification reflects the main features of the SIR-C/X-SAR images with a good classification of the urban centre but with a few vegetation pixels classified as urban in the suburbs area, deriving from ERS images. Water is very well detected thanks to its high commission accuracy. The best result was obtained with a 5 5 window as removal of the erroneously classified pixels is deeper with respect to a 3 3 window, and the classification of water improves significantly. In this way the figures in Table 4 have been obtained. The results obtained improve the confusion matrix by enhancing the commission accuracy indexes in water-covered and vegetated areas with respect to multitemporal classifications. Classification of 16,17,18 April SIR-C/X-SAR + ERS images is 77.64% better with respect to SIR-C/X-SAR alone in terms of the class water. Note that the rice fields present in SIR-C/X-SAR images are not considered as they do not appear in the ERS image; in this latter the flooded areas due to the overflow of the Ticino river are ignored for the same reason. The commission accuracy of the built-up area class remains nearly unchanged, while visually we have an improvement in the central area. With this joint analysis we obtain images that exploit the different features of the ERS and the SIR-C/X-SAR images thus providing a better discrimination of the different classes in addition to significantly reduced computation times.

5 Table 4. Classification accuracy for multisensor results. Subset Ov.Acc. C.A. (veg.) C.A. (buil.) C.A. (water) Omiss.A. (veg.) Omiss.A. (buil.) Omiss.A. (water) 16,17 Apr.+ 3 Oct. + 9 Nov.(spectral.) 16,17 Apr.+ 3 Oct. + 9 Nov. (spatial) 16,18 Apr.+ 3 Oct. + 9 Nov. (spectral) 16,18 Apr.+ 3 Oct. + 9 Nov. (spatial) 17,18 Apr.+ 3 Oct. + 9 Nov. (spectral) 17,18 Apr.+ 3 Oct. + 9 Nov. (spatial) 16,17,18 Apr. + 3 Oct. + 9 Nov. (spectral) 16,17,18 Apr. + 3 Oct. + 9 Nov. (spatial) 80.2% 77.1% 98.8% 87.4% 99.4% 40.2% 87.4% 84.4% 81.9% 98.6% 95.3% 99.6% 45.8% 95.8% 80.9% 78.4% 96.9% 84.2% 99.0% 41.1% 85.1% 84.9% 83.0% 96.7% 83.4% 99.1% 46.7% 96.7% 86.4% 84.7% 98.2% 81.7% 99.2% 49.7% 97.0% 90.9% 79.1% 98.6% 79.8% 99.2% 42.2% 92.1% 81.7% 90.1% 96.6% 90.1% 99.2% 60.0% 98.2% 86.3% 84.5% 97.8% 88.3% 99.3% 49.3% 97.9% 3.4 Urban environment characterisation using texture measures in multipolarisation data In order to get a more complete picture of the possibilities to classify urban areas in SAR images, we have also tried considering texture measures instead of intensity values themselves [12]. We took the ERS-1 image of Pavia sensed on 23 rd July 1995 and the fully polarimetric SIR-C image sensed on 14 April 1994, and computed eight texture measures: mean, variance, homogeneity, dissimilarity, contrast, entropy, correlation and second moment [13]. Here, we want to investigate how the textural measures can be of help in characterising different urban environments; as a consequence, the ground truth will only include the three subclasses of the built-up areas class, namely: city centre, residential areas and sparse buildings. The other areas are ignored. The texture measures are computed on a window pixels big, a size which maximises the overall accuracy of the classification and which is also, incidentally, corresponding to the mean size of the block in town. Our results on single polarisation images indicate that joint classification of two textural features has a significant advantage over the classification based on a single feature, while a further increase in the number of features results in smaller improvements. We have tried with up to four features and discovered that in this latter case the best combination is dissimilarity, entropy, mean and variance. For what concerns the polarisations, using the above mentioned set of textural features to jointly classify different polarisation images yields results that are best with VV polarisation (48.87% on SIR-C) among single polarisations, but improve slightly when combining more polarisations (49.66% for HV+VV). HH polarisation besides performing worse on single polarisation images (43.80%) has a negative effect when included into a joint classification (49.26% for all polarisations jointly vs. the above cited 49.66% for HV+VV; but also 46.31% for HH+HV vs % for HV alone, 48.52% for HH+VV vs % for VV alone). In general, however, all the accuracy values are low, below 50% which is an unsatisfactory value. This could be partly explained with a scarce correlation between the building density determining the ground truth and the scatterer density which instead determines the textural features in the SAR images. To what extent it is possible to get around this drawback is a matter under investigation. 4 CONCLUSIONS In this paper a series of results on neural classification of urban environments from different SAR data subsets obtained aggregating SAR data at different dates, polarisations and bands are presented. Moreover, the use of textural features for discrimination of different urban environment is also considered. From the results presented we may conclude that, on the considered data set and using the presented ARTMAP neural classification technique: Single polarisation, multitemporal data sets lead to better performance than single SAR images. Adding images in excess of the third one is however useless, as classification performances remain practically unchanged. Both omission and commission accuracy for built-up areas remains poor (around 60%)

6 regardless of the number of images included into the dataset. Multipolarisation, single date data sets lead to better performance on built-up areas in terms of omission accuracy. Commission accuracy remains about on the same level reported for multitemporal datasets. As ERS images display a good behaviour in areas covered with water and vegetation, while SIR-C/X-SAR present good classification performances on buildings and vegetation, multisensor combination is an effective way of improving classification results. Texture measures allow discrimination of different built-up classes, although with a quite poor accuracy. The joint use of two texture measures grants a significant improvement over the single-texture case, while adding further measures provides much smaller improvements. Naturally, the results can not be generalised as classification were performed only on one test site, though well known and widely experimented in our past work. Still, some interesting facts emerge and demand further investigation, such as the generally low omission accuracy on the built-up area class; one might infer the presence of a sub-class, pertaining to the built-up class, but showing significantly different characteristics in the SAR images, enough to make it unrecognisable by the built-up class prototype in the ARTMAP network. If so, a different choice of training areas for such class should be considered. REFERENCES 1. H. Scriver, J. Sehou, W. Dierking, Land cover mapping using multitemporal, dual-frequency polarimetric SAR data, proc. of IGARSS 2000, vol. 1, pp , Honolulu, HI, USA, D. Borghys, C. Perneel, M. Acheroy, Automatic detection of built-up areas in high-resolution polarimetric SAR images, Pattern Recognition Letters, no. 23, pp , Y. Dong, B. Forster, and C. Ticehurst, A new decomposition of radar polarization signatures, IEEE Trans. Geosci. Remote Sensing, vol. 36, pp , May P. Lombardo, T. Macrì Pellizzeri, A. Tomasuolo, Classification of polarimetric SAR images of suburban areas using joint annealed segmentation and H/A/α decomposition, proc. IEEE/ISPRS Joint Workshp Rem. Sens. & Data Fusion over Urban Areas, Rome, Italy, pp , 8-9 Nov E. Costamagna, P. Gamba, P. Lombardo, G. Chinino: Statistical analysis and neuro-fuzzy classification of polarimetric SAR images of urban areas, Proc. of the ERS/ENVISAT Symposium, Gotheborg, Sweden, Oct G. Liu, H. Xiong, S. Huang, Study on segmentation and interpretation of multi-look polarimetric SAR images, Int. J. Remote Sensing,, vol. 21, no. 8, pp , F. Dell Acqua, P. Gamba, P. Lombardo, T. Macrì Pellizzeri, D. Mazzola: Multiband SAR classification using contextual analysis: annealing segmentation vs. a neural kernel-based approach, Proc. of IGARSS 02, Toronto (CAN), June 2002, Vol. V, pp P. Gamba, B. Houshmand: An efficient neural classification chain for optical and SAR urban images, International Journal of Remote Sensing, Vol. 22, n. 8, pp , May G. Amici, F. Dell Acqua, P. Gamba, G. Pulina: Fuzzy, neural and neuro-fuzzy classification of pre- and postevent SAR images for flood monitoring and disaster mitigation, Proc. of the First International Workshop on Multi-temporal Analysis of Remote Sensing Images, Trento, Italy, Sept B. Mannan, J. Roy, K.A. Ray, Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images. Intl. J. Remote Sensing, 19 (4), pp , G.A. Carpenter, M.N. Gjaja, S. Gopal, Woodcock, C.E., ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data. IEEE Trans. Geoscience and Remote Sensing, 35, , F. Dell Acqua and P. Gamba, Spatial analysis of satellite SAR for urban applications, Proc. of the 2 nd Pattern Recognition and Remote Sensing Workshop, Niagara Falls, Canada, pp , 16 Aug R.M. Haralick, K. Shammugam, I. Dinstein, Textural features for image classification, IEEE Trans. Systems, Manufact. Cybernet., Vol. 3, no. 6, pp , 1988.