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

被引：58

作者：

Kang, Wenchao ^{[1
,2
,3
]}

Xiang, Yuming ^{[1
,2
,3
]}

Wang, Feng ^{[2
,3
]}

Wan, Ling ^{[1
,2
,3
]}

You, Hongjian ^{[1
,2
,3
]}

机构：

[1] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 101408, Peoples R China

[2] Chinese Acad Sci, Inst Elect, Beijing 100190, Peoples R China

[3] Key Lab Technol Geospatial Informat Proc & Applic, Beijing 100190, Peoples R China

来源：

SENSORS | 2018年 / 18卷 / 09期

基金：

国家重点研发计划;

关键词：

SAR; flood detection; FCN; GF-3; satellite;

D O I：

10.3390/s18092915

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：22

共 34 条

[1] Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network [J].