Flood Mapping Using Relevance Vector Machine and SAR Data: A Case Study from Aqqala, Iran

被引:50
作者
Sharifi, Alireza [1 ]
机构
[1] Shahid Rajaee Teacher Training Univ, Fac Civil Engn, Dept Surveying Engn, Tehran 16785136, Iran
关键词
Flood mapping; SAR; Remote sensing; Machine learning; Classification; GOLESTAN PROVINCE; AREAS; IMAGERY;
D O I
10.1007/s12524-020-01155-y
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The use of satellite imagery to monitor flood areas is essential to determine the damage and prevent related problems in the future. This paper examines thresholding and unsupervised classification for flood mapping using Sentinel-1 SAR image. Thresholding helps us to determine over-detection and under-detection regions in the flooded areas, and so, gamma distribution is used to select the thresholds. Also, the relevance vector machine (RVM) and the object-based classification method have been used for classification. The RVM algorithm obtained better results with overall accuracy = 0.89 andk = 0.95, while for the object-based classification method, these values were 0.87 and 0.91, respectively. According to the results, over- and under-detection occurred in flat areas and man-made structures, respectively. The results demonstrate a great potential of radar imagery for operational detection and delimitation of water in flood risk areas. The automation of satellite radar data processing operation has been tested, and it shows a potential for optimising the system of monitoring and early detection of flood risk.
引用
收藏
页码:1289 / 1296
页数:8
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