Understanding ERS Coherence over Urban Areas

Understanding ERS Coherence over Urban Areas A. Fanelli(1), M. Santoro(2), A. Vitale(1), P. Murino(1) and J. Askne(2) (1)

Dipartimento di Scienza e Ingegneria dello Spazio “Luigi G. Napolitano” Università degli Studi di Napoli “Federico II” P.le V. Tecchio 80, 80125 Naples, Italy Mail: [email protected] (2)

Department of Radio and Space Science, Chalmers University of Technology, S-412 96, Gothenburg, Sweden Mail: [email protected]

ABSTRACT C-band SAR interferometry has been widely used for several Earth related studies (topography, volcanology, forestry etc.). Nevertheless, not much attention has been paid to urban areas and coherence has been used for classification purposes only. In this study we focused on the meaning of coherence of an urban area and performed several analysis in order to understand what causes decorrelation. Two test sites have been considered: Gothenburg in Sweden and Naples in Italy. For both cities we had a reasonable number of coherence images with one-day and long-period acquisition interval. SAR amplitude images have also been considered. Using RGB colour composite combinations of SAR and InSAR imagery, we could relate the level of coherence of a part of a city either to topography, InSAR system geometry and position of the targets or to image processing or to structural typologies. INTRODUCTION In the recent decades the discussion on urban areas and their monitoring has been brought up. The increase of population and mass migrations result in the expansion of already existing urban settlements as well as in the creation of new ones. The consequences are towns and cities with clusters of buildings for several purposes, not always rationally planned though. The possibility of having a global view of an urban area with a good resolution make spaceborne remote sensing imagery a valid instrument for urban area mapping and settlement analysis [1]. Moreover, periodical acquisition of images over the same area allows regular and continuous monitoring of a specific area over a certain number of years. Compared to optical sensors, SAR does not suffer from limitations due to cloud cover and darkness, therefore SAR imagery can be useful in urban-oriented studies of any part of the Earth. Backscatter from urban areas is generally high and it depends on size, shape, orientation and typology of the imaged structures [1]. At C-band the relation between a target and its backscatter has been studied both from a qualitative [2] and a quantitative [3-4] point of view. Recently, C-band SAR interferometry, InSAR, has been acknowledged to be a valuable technique for land-cover characterisation studies. Concerning urban areas, coherence has been shown to be a fundamental parameter for mapping purposes [5-8]. However, the meaning of coherence of an urban area has not been analysed in depth; moreover, it has not been completely clarified what determines the decorrelation of man-made features [9]. Urban areas are the only temporary stable land-cover; therefore, coherence, both on one-day and long-term acquisition interval basis, is generally high, equal to one in some cases. In this study we want to understand what are the causes of decorrelation of an inhomogeneous land-cover such as the urban one and which parameters have influence. This should then clarify the concept of coherence of an urban area. TEST SITES This study focuses on two urban areas located in Europe totally different from an urbanistic point of view: the city of Naples, Italy (Lat. N 40.5, Long E 14.1 for the centre) and the city of Gothenburg, Sweden (Lat. N 57.6, Long. E 11.9 for the centre). The city of Naples consists of a major city surrounded by metropolitan towns, continuously growing in size and population. The cities have never developed according to periodic urban plans and the results are clusters of buildings with different sizes and shapes all over the area. Residential areas are found both in Naples city centre and on the outskirts; they surround industries and several infrastructures (airport, harbour, railways, open areas for different purposes etc.). Green areas have been left where the urban expansion has not arrived. The topography in the area is

mixed. It is relatively flat in the eastern and the northern part. A constant slope is present in the old part of the city; slopes of ancient volcanic areas are located in the western part. The Swedish site is characterised by a rather small and compact city centre (approximately 150 years old), surrounded by green areas and several modern built-up areas and infrastructures (harbour, railway, hospitals etc.). Industrial and residential areas with high population density are located in the northern part; residential areas both densely and sparsely built-up are located at the southern edge of the city. Gothenburg has always expanded according to townplanning schemes and newly built-up areas look homogeneous and well shaped. The urban area is built on a relatively flat surface even though some hills with reasonable slopes are present in the city centre. IMAGERY The set of images over Naples consisted of six pairs acquired by ERS-1/-2 during the “tandem” mode between 1995 and 2000. For Gothenburg we considered five “tandem” pairs, acquired in 1995 and 1996. Besides “tandem” pairs, long-time acquisition interval pairs were considered; because of decorrelation, only pairs that had a normal component of the baseline below 300 meters were used in this study. Hence, we had available thirteen and three long-term coherence images of Naples and Gothenburg, respectively. In Tables 1 and 2, the temporal interval between acquisitions and the normal component of the baseline are listed for each pair. For each pair co-registration between master and slave image was performed at sub-pixel level. Before estimating coherence, azimuth filtering [10] and wave-number shift filtering [11] were carried out in order to reduce the Table 1 – Imagery data set characteristics for the Naples test site. MASTER

SLAVE

DAYS

13th December 1995 13th December 1995 13th December 1995 13th December 1995 14th December 1995 14th December 1995 21st May 1997 21st May 1997 21st May 1997 30th July 1997 30th July 1997 th 8 October 1997 8th October 1997 8th October 1997 26th May 1999 26th May 1999 26th May 1999 1st March 2000 31st July 1997

14th December 1995 8th October 1997 26th May 1999 27th May 1999 9th October 1997 27th May 1999 22nd May 1997 30th July 1997 31st July 1997 31st July 1997 22nd May 1997 9th October 1997 22nd May 1997 th 14 December 1995 27th May 1999 9th October 1997 14th December 1995 2nd March 2000 22nd May 1997

1 day 666 days 1260 days 1261 days 665 days 1260 days 1 day 70 days 71 days 1 day 69 days 1 day 139 days 664 days 1 day 594 days 1259 days 1 day 70 days

NORMAL BASELINE [m] 221 164 -48 -124 -52 97 -93 -15 -131 132 97 -282 -218 -230 82 128 179 -105 41

Table 2 - Imagery data set characteristics for the Gothenburg test site. MASTER

SLAVE

DAYS

13th August 1995 22nd October 1995 10th March 1996 14th April 1996 23rd June 1996 14th April 1996 23rd June 1996 23rd October 1995

14th August 1995 23rd October 1995 11th March 1996 15th April 1996 24th June 1996 14th August 1995 14th April 1996 11th March 1996

1 1 1 1 1 244 70 140

NORMAL BASELINE [m] 24 -91 -43 79 89 10 -17 -8

decorrelation due to InSAR geometry. Coherence was computed using both a 3 by 15 and a 5 by 25 window; compensation for the local topography was applied. Since this study is focused on a high-coherent land-cover such as urban areas, bias removal was not performed; coherence values of 0.135 measured over water areas show that the bias has no practical effect. SAR amplitude images have been averaged five times in the azimuth direction in order to reduce speckle and to have square pixels. Same averaging operation was performed on coherence images. All SAR and InSAR images of Naples have been kept in the slant range geometry, having a pixel size of 20 by 20 meters. For Gothenburg, there was available a DEM provided by the Swedish National Land Survey, having a pixel size of 50 by 50 meters. SAR and InSAR imagery have been geocoded using a resampled version of the original DEM, having a 25 by 25 meters pixel size. Maps in digital and paper format have been used for visual aid and to identify urban structures. For the centre city of Naples there was available a digital map at a scale of 1:1000. METHODOLOGY In this work SAR and InSAR images have been combined in order to have two kinds of RGB colour composites. One combination consisted of coherence images only. The images were combined in two different ways as follows: One-day coherence image (red channel) and two long-term coherence images (green and blue channels). The three coherence images have a common SAR master image. Three one-day coherence images. The second combination included three images having a master SAR image in common as follows: One-day coherence (red), long-term coherence (green) and SAR amplitude (blue). We preferred to analyse RGB colour composites rather than single amplitude and/or coherence images in order to have more information simultaneously available and to have a broader comprehension of what properties of the imaged scene are contained in SAR and InSAR images. RESULTS In this section results from RGB composites analysis are presented, the reasons for decorrelation both at target level and concerning clusters of buildings being summarised. Target Analysis In RGB composites very bright and white points are clearly discernible. These points represent areas where one-day coherence, long-term coherence and SAR amplitude are high. A major part of them are located in the same position in all the images (Figg. 1, 3). When SAR, InSAR and RGB images have been compared with maps of the cities, it has been possible to determine the position of such points and to extract them for further analysis. By means of pictures and in situ observations, we could identify several kinds of targets, standing clearly out from the surrounding areas (Fig. 2). Such targets are industrial blocks, big tanks, metallic containers and piers in harbours, big shopping malls and residential buildings, schools, sport centres and churches. All these building typologies have a simple structure, cubic or cylindrical, so that the backscatter is mainly due to single- or double-bounce, being, moreover, constant in time. The coherence of these targets should be therefore equal to one. Measured values are generally between 0.9 and 1, which could be due to the InSAR processing chain or to the size of the estimation window. In fact, some buildings might be smaller than the window size so that some decorrelation from areas adjacent the target might occur. Area Analysis When RGB images have been analysed in order to find and understand what causes decorrelation in an urban environment, much information was obtained about the configuration of both cities. In the colour composites of Naples, some areas characterised by natural layover (the hill of Posillipo, the edges of the craters of Agnano, Pianura and Astroni) or covered with vegetation (the Astroni and the Capodimonte woods) are evident, being always very dark (Fig. 1). On the other hand, in Gothenburg imagery, there is a big similarity between rocky areas and very dense urban settlements, both appearing very bright. Suburban residential quarters are generally a mixture of buildings and green areas. In RGB images, such areas are characterised by white dots (high coherence and SAR amplitude), corresponding to buildings, on a background that has a colour dependant on the specific conditions of the vegetation at the acquisition time of the satellite (Fig 3). As previously mentioned, dense built-up areas appear generally bright because of the high temporal stability and the high backscatter. Nevertheless, the centre of Naples, a densely urbanised part of the city, never shows high coherence in our data set (values around 0.5). Several reasons could be found for such behaviour among which: • Atmospheric events;

• The length of the normal component of the baseline; • A constantly varying slope, difficult to compensate for, because of the size of the window used to compute coherence and because of the resolution of ERS imagery.

Astroni Capodimonte

Hill of Posillipo

City centre

Fig. 1 - Naples test site. RGB composite of one-day coherence (May ‘97) in red, long-term coherence (21 May ’97 ERS-1 – 30 July ’97 ERS-1) in green and long-term coherence (21 May ’97 ERS-1 – 31 July ’97 ERS-2) in blue. The image covers an area of about 300 km2

Airport

(a) (b) Fig. 2 - (a) Zoom in a long-term coherence image of the centre of Naples. (b) Typical example of commercial buildings that are represented in the box of fig. 2 (a) and with coherence values of 0.85

Old city center

Haga

Guldheden Chalmers

Fig. 3 - Gothenburg test site. RGB composite of one-day coherence (October ‘95) in red, long-term coherence (14 April ‘96 ERS-1 – 14 August ‘95 ERS-2) in green and SAR amplitude (14 August ’95 ERS-2) in blue. The image covers an area of about 500 km2. In Figure 4 the effect of baseline and topography are clearly visible. The old city centre has a strong slope going from 0 m at the sea level to 400 m on the Camaldoli hill. This slope becomes evident because of two reasons: 1. Effect of the baseline. Depending if we put in the red or in the green or in the blue channel the tandem pair acquired in 1999, that has the smaller baseline, the old city centre appears in red, green or blue, respectively. 2. Effect of uncompensated topography. The combined effect of the direction of illumination and the position of the old city centre is therefore fundamental to explain the decorrelation. Combined effects of topography and baseline have been noticed also in Gothenburg RGB imagery (see Figure 5). Areas built on the side of hills not facing the radar suffer from higher decorrelation for increasing normal component of the baseline (Johanneberg, Guldheden and Vallgraven residential areas). Coherence of areas built on a flat ground, instead, is independent of/from the baseline’s length (Haga district). Finally, disposition and shape of buildings have been found to have an effect on coherence. In Figure 6 we report a plot that compares coherence of several areas in Gothenburg. Residential quarters in the central part of the city, scarcely vegetated, with an non-regular disposition of buildings and large spaces between them (Johanneberg and Guldheden) are less coherent than denser built-up areas with a regular net of buildings. The reason is larger phase variability where buildings are distant from each other. In the second category a further distinction can be made. Clusters of buildings with simple shapes and being all similar (Haga, old city centre, Vallgraven, residential area of Solängen in Mölndal) are more coherent than areas with different typologies of buildings (Chalmers, Linnestaden and Lorensberg). Once again, the higher phase variability in the last areas could explain lower coherence values.

Fig. 4 - The RGB colour composites are a combination of one-day coherence images acquired in December 1995, July 1997 and May 1999, having normal component of the baseline equal to 221 m, 132 m and 82 m, respectively. Area covered equal to about 300 km2

Fig. 5 - Comparison of coherence values in areas with topography for increasing baseline. The upper line is a reference for a homogeneous dense built-up area on a surface with no topography. Only one-day coherence images are included since long-term pairs had normal component of the baseline below 20 meter. Each area has been extracted from images using digital and paper maps and the mean value of coherence has been computed. Size of the areas between 0.1 km2 and 0.2 km2.

Fig. 6 - Coherence plots for several built-up areas in Gothenburg. The normal component of the baseline is equal to 24 m so that effects due to topography are negligible. Each area has been extracted from images using digital and paper maps. Mean value and standard deviation have been reported. Size of the areas between 0.1 km2 and 0.2 km2.

CONCLUSIONS In this work factors that contribute to the decorrelation in urban areas have been studied. In our analysis, RGB colour composites of one-day coherence, long-term coherence and SAR amplitude images have been preferred to single components because they synthesised information provided by each combined image and made visible details otherwise hard to notice. In all colour imagery of both test sites a number of bright and white points have been identified, corresponding to structures with simple shapes. These targets are characterised by little decorrelation, which could be related to image processing or window size. The amount of vegetation between buildings could explain the higher decorrelation measured in suburban residential areas than in centrally located and densely built-up areas. The combined effect of topography, length of the normal component of the baseline and SAR viewing conditions has been clearly shown in the old city centre of Naples and in some quarters of Gothenburg where coherence was lower in comparison to surrounding areas built on flat ground. The disposition and the shape of clusters of buildings have also an effect on coherence; a non-regular disposition of buildings and the variety of structural typologies have been shown to decrease coherence slightly, because of uncompensated topography introduced by buildings. ACKNOWLEDGEMENTS InSAR processing has been performed using the ATLANTIS EarthView InSAR 1.1 software for the imagery over Naples. For Gothenburg processing of images was carried out with the ISAR Toolbox from ESA, the DIAPASON software from CNES and ML-coherence estimation program by Dr. Patrik Dammert. Funding for the research was provided by the Swedish National Space Board and by the University of Naples “Federico II”. SAR images of Gothenburg and Naples were provided by the European Space Agency through AOT-S301 and AOE-514, respectively. REFERENCES [1] F. Henderson and Z. Xia, “Radar Applications in Urban Analysis, Settlement Detection and Population Estimation,” Manual of Remote Sensing, Third Edition, vol. 2, edited by F. M. Henderson and A. J. Lewis. [2] B. Dousset, “Synthetic Aperture Radar imaging of urban surfaces: a case study,” Proceedings of IGARSS’95, 1014 July, Firenze, pp. 2092-2096, 1995. [3] X. Becquey, N. Cambou, D. J. Weydahl and T. Wahl, “Analyse de ponctuels brillants et de leur environnement dans une série d’images SAR ERS-1,” Proceedings of Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applaications, 10-13 October 1995, Toulouse, pp. 535-538. [4] C. Gouinaud and H. Maître, “Critères de classification pour les images readar d’agglomération,” Proceedings of Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applaications, 10-13 October 1995, Toulouse, pp. 539-548. [5] T. Strozzi and U. Wegmüller, “Delimitation of Urban Areas with SAR Interferometry,” Proceedings of IGARSS’98, 6-10 July, Seattle, pp. 1632-1634, 1998. [6] F. Del Frate, J. Lichtenegger and D. Solimini, “Monitoring urban areas by using ERS-SAR data and neural networks algorithms,” Proceedings of IGARSS’99, 28 June - 2 July, Hamburg, pp. 2696-2698, 1999. [7] M. Santoro, A. Fanelli, J. Askne and P. Murino, “Monitoring urban areas by means of coherence levels,” in press. [8] M. Santoro, A. Fanelli, J. Askne and P. Murino, “Urban areas classification with SAR and InSAR signatures,” Proceedings of EUSAR2000, 3rd European Conference on Synthetic Aperture Radar, Munich, 23-25 May 2000, pp. 647-650. [9] S. Usai and R. Klees, “SAR Interferometry on a Very Long Time Scale: A Study of the Interferometric Characteristics of Man-Made Features,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, pp. 2118-2123, 1999. [10] M. Schwäbisch and D. Geudtner, “Improvement of Phase and Coherence Map Quality Using Azimuth Prefiltering: Examples from ERS-1 and X-SAR,” Proceedings of IGARSS’95, Firenze, 10-14 July 1995, pp. 205207, 1995. [11] F. Gatelli, A. M. Guarnieri, F. Parizzi, P. Pasquali, C. Prati and F. Rocca, “The Wavenumber Shift in SAR Interferometry,” IEEE Transactions on Geoscience and Remote Sensing, vol. 32 , pp. 855-865, 1994.