BUILT-UP AREAS MAPPING AT GLOBAL SCALE BASED ON ADAPATIVE PARAMETRIC THRESHOLDING OF SENTINEL-1 INTENSITY & COHERENCE TIME SERIES

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1 BUILT-UP AREAS MAPPING AT GLOBAL SCALE BASED ON ADAPATIVE PARAMETRIC THRESHOLDING OF SENTINEL-1 INTENSITY & COHERENCE TIME SERIES M. Chini, R. Pelich, R. Hostache, P. Matgen MultiTemp 2017 June 27-29, Bruges (Belgium)

2 WORKING HYPOTHESIS I. Building SAR backscattering is high. II. InSAR coherence in urban areas is high. SAR Intensity InSAR coherence

3 WORKING HYPOTHESIS Urban areas appear very bright (very high backscattering). Smooth water surface covering the terrain reflects the radar signal in the specular direction (very low backscatter). Number of pixels Backscattering [db]

4 CLASSIFICATION STRATEGY SAR intensity Statistical based algorithms typically parameterize distribution functions to assign pixels to 2 or more semantic classes of interest. Classes of interest often represent only a small fraction of an entire SAR scene: difficulty in parameterizing such distributions functions. A sufficiently high percentage of pixels is typically required to estimate a reliable and robust distribution function that can be used for accurate detection of land cover class. Thus, the capability to detect classes cannot only rely on spectral signatures but also on the prior probability of the target class.

5 CLASSIFICATION STRATEGY We propose a Hierarchical Split-Based Approach (HSBA) where the size of tiles is not fixed a priori, already developed to map flooded areas. The algorithm has been developed in the framework of the Urban Round- Robin exercise, supported by the European Space Agency (ESA) through the ESA Land Cover Climate Change Initiative (CCI), and tested on Sentinel-1 data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. M. Chini, R. Hostache, L. Giustarini, P. Matgen, A Hierarchical Split-Based Approach (HSBA) for parametric thresholding of SAR images: flood inundation as a test case, IEEE Transactions on Geoscience and Remote Sensing (Under review).

6 CLASSIFICATION STRATEGY HSBA We assume that the image is composed of three main classes (Urban, Water and Other Classes) We have adopted a two-steps statistical approach: First, we apply the algorithm to delineate the water class (HSBA-Water) Second, we apply the algorithm to identify the urban areas (HSBA- Urban) in the remaining image where water pixels have been masked out The algorithm delineating Water or Urban areas is composed of two steps: Hierarchical Split-Based Approach (HSBA) to select bimodal areas Based on the statistics of the selected bimodal areas, we use a hybrid methodology, which combines distributions fitting, thresholding and region growing, for the automatic detection of the class of interest in the entire scene

7 CLASSIFICATION STRATEGY HSBA SAR image L 0 L 1 L 2 L 3

8 CLASSIFICATION STRATEGY HSBA Class distributions of SAR images are assumed Gaussian: (yy μμ 1 ) 2 h yy = GG 1 yy + GG 2 (yy) = AA 1 ee 2ssss2 1 (yy μμ 2 ) 2 + AA 2 ee 2ssss2 To fit the distributions the Levenberg-Marquardt algorithm is used Rules to select tiles: 1) Tile histogram is bimodal, Ashman D > 2 AAAA (h (yy)) = 2 μμ 1 μμ 2 ssss ssss 2 2) Smallest class is more that 10% of the tile.

9 CLASSIFICATION STRATEGY Region Growing The selection of the threshold can benefit from the combination of the contextual information of the image with its intensity information. Here, we use a region growing approach that assumes that pixels constituting the target class are clustered rather than randomly spread out over the entire image.

10 SAR IMAGING MECHANISM Double-bounce & Foreshortening Foreshortening Double-Bounce θ Double- Bounce φ Range direction Azimuth direction Higher is θ, higher is the double-bounce backscattering. Lower is φ, higher is the double-bounce backscattering.

11 TEMPORAL FEATURES Intensity & coherence Temporal Average Intensity (TAI) To reduce speckle without reducing the spatial resolution. One image per month (one year of acquisitions). Temporal Average Coherence (TAC) It is extracted form InSAR coherence maps with one month temporal baseline (one year of acquisitions). Purpose: catch vegetation changes during different seasons.

12 OVER AND UNDER DETECTION Using only SAR intensity Over detection: Vegetated areas Solution: InSAR coherence Foreshortening in mountainous areas Solution: DEM to extract local incidence angle Under detection: Unfavorable Line of Sight (Los) Solution: Ascending and descending orbit paths

13 OVER DETECTION Vegetated areas Intensity Egypt Intensity Coherence Coherence

14 OVER DETECTION Vegetated areas Intensity HSBA Coherence Intensity HSBA Coherence

15 OVER DETECTION Foreshortening (FS) in mountainous areas FS CC removal filtering HSBA Coherence Intensity

16 UNDER DETECTION Unfavorable Line of Sight Descending Building map / DESC Ascending Building map / ASC

17 PROCESSING CHAIN 1) Temporal Average of Intensity and Coherence extraction 2) Foreshortening removal from TAI 3) Water classification (HSBA) and removal from TAI 4) Buildings classification by HSBA 5) Over detection (vegetation) removal using TAC thresholding 6) Building maps from ascending and descending orbit merge

18 TEST CASES Areas of interest: Portugal, Sicily (Italy) and Egypt One image per month over one year of acquisitions

19 RESULTS Egypt land cover cci built-up areas. proposed method built-up areas.

20 RESULTS Egypt complementarity of the building maps obtained from the ascending and descending orbits. areas classified as building within both image tracks. areas classified as building in one image track SAR Intensity Buildings map

21 RESULTS Egypt

22 RESULTS Portugal degree of imperviousness 2012 proposed method built-up areas

23 RESULTS Portugal SAR Intensity Proposed method buildings map Degree of imperviousness 2012, Copernicus Land Monitoring Service

24 RESULTS Sicily (Italy) degree of imperviousness 2012 proposed method built-up areas Greenhouses

25 CONCLUSIONS This procedure renders the urban mapping approach independent of the different technical characteristics of the SAR scene (e.g. spatial resolution or percentage of urban extension respect to extension of the image). The presumed hypotheses are rather simple but valid for almost all buildings in our test cases. Perform an in-depth evaluation of our methodology: a comparison with the building map developed by the DLR s Global Urban Footprint (GUF) project.

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