Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images

被引:147
|
作者
Amitrano, Donato [1 ]
Di Martino, Gerardo [1 ]
Iodice, Antonio [1 ]
Riccio, Daniele [1 ]
Ruello, Giuseppe [1 ]
机构
[1] Univ Napoli Federico II, Dept Elect Engn & Informat Technol, I-80125 Naples, Italy
来源
关键词
Classification; co-occurrence texture; flooding; fuzzy systems; synthetic aperture radar (SAR); WATER INDEX NDWI; SEMIARID REGIONS; RESERVOIRS; SYSTEM; EXTENT;
D O I
10.1109/TGRS.2018.2797536
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
We present a new methodology for rapid flood mapping exploiting Sentinel-1 synthetic aperture radar data. In particular, we propose the usage of ground range detected (GRD) images, i.e., preprocessed products made available by the European Space Agency, which can be quickly treated for information extraction through simple and poorly demanding algorithms. The proposed framework is based on two processing levels providing event maps with increasing resolution. The first level exploits classic co-occurrence texture measures combined with amplitude information in a fuzzy classification system avoiding the critical step of thresholding. The second level consists of a change-detection approach applied to the full resolution GRD product. The discussion is supported by several experiments demonstrating the potentiality of the proposed methodology, which is particularly oriented toward the end-user community.
引用
收藏
页码:3290 / 3299
页数:10
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