Improving SAR-based flood detection in arid regions using texture features

被引：2

作者：

Ritushree, D. K. ^{[1
]}

Garg, Shagun ^{[2
]}

Dasgupta, Antara ^{[3
]}

Martinis, Sandro ^{[4
]}

Selvakumaran, Sivasakthy ^{[5
]}

Motagh, Mahdi ^{[6
]}

机构：

[1] GFZ German Res Ctr Geosci, RemoteSensing & Geoinformat Geodesy, Potsdam, Germany

[2] Univ Cambridge, Dept Engn, Future Infrastruct & Built Environm FIBE, Cambridge, England

[3] Univ Osnabruck, Inst Informat, Remote Sensing Working Grp, Osnabruck, Germany

[4] German Aerosp Ctr DLR, German Remote Sensing Data Ctr DFD, Oberpfaffenhonefen, Germany

[5] Univ Cambridge, Dept Engn, Cambridge, England

[6] Leibniz Univ Hannover, Inst Photogrammetry & Geoinformat, Hannover, Germany

来源：

2023 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE FOR GEOANALYTICS AND REMOTE SENSING, MIGARS | 2023年

基金：

英国工程与自然科学研究理事会;

关键词：

Flood mapping; SAR; texture; Random Forest;

D O I：

10.1109/MIGARS57353.2023.10064526

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Flood monitoring in arid regions is challenging using Synthetic Aperture Radar (SAR) due to the similar backscatter of water and dry sand in surrounding areas. Since textural information is abundant in SAR images, this study investigates the added value of texture in SAR-based flood detection by providing it as auxiliary information for flood delineation. Results show that texture enhanced SAR images in VH polarization substantially underpredicts the flooded area, so adding texture does not improve the classification accuracy. However, using both polarization (VV and VH) produce similar to 26% higher overall accuracy for flood detection in arid regions.

引用

页码：175 / 178

页数：4

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