Automatic SAR-based flood detection using hierarchical tile-ranking thresholding and fuzzy logic

Abstract

Given the proven effectiveness of the split-based approach (SBA) for SAR image analysis in literature, the objective of this article focuses on designing a more efficient and robust version of the SBA for applications in the context of rapid flood mapping. A hierarchical tile-ranking SBA is proposed in this paper which is combined with a previous multilevel tile contrast analysis to significantly reduce the amount of data for the estimation of global threshold. A separability test is further applied to reject badly located tiles. The classification is optimized by merging pixel backscatter values, cluster size and local slope into a fuzzy-logic based post-classification framework. The proposed method was tested on Sentinel-1 SAR data acquired over Lake Liambezi in the Caprivi Strip of Namibia and validated with respect to a Landsat-8 scene. Compared to tiles selected by the conventional SBA the proposed method automatically select better relevant ones and the classification is more robust with less misclassification of water-lookalikes.