Multi-source remote sensing image flood disaster information extraction and change analysis based on improved U-Net network

doi:10.13474/j.cnki.11-2246.2022.0005

Abstract

Abstract: Information extraction and change analysis based on remote sensing images is an important method for detecting changes on the earth's surface, and it has a wide range of applications in urban planning, environmental monitoring, disaster assessment. SAR can penetrate clouds to achieve ground observation during floods, but side-view imaging will cause overlaps and shadows along with terrain undulations. DEM can provide elevation information of ground points, reflecting the undulations of the terrain, but cannot provide surface information. In order to achieve high-precision and high-efficiency extraction of disaster information for the disaster area and degree of the flood area, this paper proposes a change detection method based on U-Net fusion of multi-source remote sensing images. Experimental results show that disaster flood information with high spatial resolution can be extracted efficiently by the method performed in this paper.

Key words: U-Net, deep learning, change detection, data enhancement, GF-3

CLC Number:

P237

CHE Zijie, GAO Fei, WU Zhaofu, LI Zhenxuan. Multi-source remote sensing image flood disaster information extraction and change analysis based on improved U-Net network[J]. Bulletin of Surveying and Mapping, 2022, 0(1): 26-32.

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