基于改进U-Net网络的多源遥感影像洪涝灾害信息提取与变化分析

基于遥感影像的信息提取与变化分析是检测地球表面变化的重要方法，在城市规划、环境监测、灾害评估等方面应用广泛。合成孔径雷达（SAR）可以穿透云层，实现洪水期的对地观测，但侧视成像会随地形起伏产生叠掩、阴影等现象。DEM可以提供地面点的高程信息，反映地形的起伏变化，但无法提供地物信息。因此，本文提出了一种融合多源遥感影像、基于改进U-Net网络的变化检测方法；并通过试验证明了该方法可以实现高精度、高效的洪涝灾害信息提取。

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

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.