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基于全卷积网络的高分三号 SAR 图像洪水检测。

Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks.

机构信息

School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China.

Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.

出版信息

Sensors (Basel). 2018 Sep 2;18(9):2915. doi: 10.3390/s18092915.

Abstract

Emergency flood monitoring and rescue need to first detect flood areas. This paper provides a fast and novel flood detection method and applies it to Gaofen-3 SAR images. The fully convolutional network (FCN), a variant of VGG16, is utilized for flood mapping in this paper. Considering the requirement of flood detection, we fine-tune the model to get higher accuracy results with shorter training time and fewer training samples. Compared with state-of-the-art methods, our proposed algorithm not only gives robust and accurate detection results but also significantly reduces the detection time.

摘要

应急洪水监测和救援需要首先检测洪水区域。本文提供了一种快速新颖的洪水检测方法,并将其应用于高分三号 SAR 图像。本文利用全卷积网络(FCN),即 VGG16 的变体,进行洪水制图。考虑到洪水检测的要求,我们对模型进行了微调,以在更短的训练时间和更少的训练样本的情况下获得更高的准确性结果。与最先进的方法相比,我们提出的算法不仅给出了稳健和准确的检测结果,而且显著减少了检测时间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/346a/6165191/9f98b19692a9/sensors-18-02915-g001a.jpg

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