Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning

Proceedings

article

Mohammadali Abbasi¹  Reza Shah-Hosseini¹  Mohammad Aghdami-Nia¹ 

[1] School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran

关键词: flood detection;    remote sensing;    SAR;    Sentinel-1;    deep learning;    U-Net;   

DOI  :  10.3390/IECG2022-14069

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Flood is one of the most damaging natural hazards, and timely detection of it is very important to save human lives and assess the level of damage. The occurrence of floods in cloudy weather conditions makes the use of radar-based sensors for real-time flood mapping inevitable. In the present study, the ETCI 2021 flood event detection competition dataset, organized by the NASA Advanced Concepts and Implementation Team in collaboration with the IEEE GRSS Geoscience Informatics Technical Committee, has been used. Moreover, we have utilized the U-Net and X-Net architecture as a segmentation model to map flooded regions. This study aimed to identify the optimum polarization of the Sentinel-1 satellite for flood detection. By examining and comparing the obtained results, it was observed that the VV polarization offered better results in both models. Furthermore, U-Net had a better performance than X-Net in both polarizations.

【 授权许可】

CC BY