The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1697–1703, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1697-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1697–1703, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1697-2020

  22 Aug 2020

22 Aug 2020

SENTINEL-1 BASED FLOOD MAPPING USING INTERFEROMETRIC COHERENCE AND INTENSITY CHANGE DETECTION APPROACH

I. Papila, U. Alganci, and E. Sertel I. Papila et al.
  • ITU, Research and Application Center for Satellite Communications and Remote Sensing (CSCRS), 80626 Maslak Istanbul, Turkey

Keywords: Flood Mapping, Interferometric Coherence, SAR Intensity, Region Growing Algorithm, Change Detection, Sentinel-1, Spot-6

Abstract. This study presents a semi-automatic algorithm for mapping floods. Both Optical and Synthetic Aperture Radar (SAR) data are used to observe the flood that hit the Cukurova region of Adana (Turkey) in 2019. The performance of the interferometric coherence in complementing intensity component of SAR data is investigated for mapping the floods occurred in agricultural and urban environments. There was no ground truth data available from the flooded area, thus classification result of optical satellite image is used as a seed for the region growing algorithm that defines the classes according to a threshold value. The advantage of using both intensity and coherence change detection is verified with the results. The results have been evaluated through very high-resolution SPOT-6 optical image which acquired simultaneously with Sentinel-1B SAR image. The comparison with the SPOT-6 data results shows that the proposed approach can map flooded areas with acceptable accuracy using the SAR data from Sentinel-1 satellite mission. Highly affected agricultural areas along with the river line could be mapped both by optical and SAR analysis. Comparison of results from VV and VH polarization provided that cross-polarization VH has a very little effect on flood mapping. The proposed algorithm successfully distinguishes the classes among the affected region, especially in urban areas.