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

Abstract

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 and k = 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.