Urban flood mapping by fully mining and adaptive fusion of the polarimetric and spatial information of Sentinel-1 images

Highly destructive flood disasters have occurred frequently recently. Related to this, accurate mapping of flood areas is a necessary undertaking that helps to understand the temporal and spatial evolution patterns of floods. Thus, this paper proposes a novel, unsupervised multi-scale machine learning (ML) approach for urban flood mapping with SAR images from the perspective of information mining and fusion. Considering the complexity of surface objects in urban scenes, the proposed approach first extracts and fuses multiple types of features, such as polarization, pseudo-color, and spatial features, from pre-flood and post-flood SAR images to enhance distinguishability of water bodies. In particular, some new pseudo-color features are constructed here for SAR images through pseudo-color synthesis and color space transformation. On this basis, a flood probability map (FPM) is generated, and multi-scale superpixel segmentation is performed on it. Then, an ML-based unsupervised classification model assisted by uncertainty analysis based on the Gaussian mixture model is designed and implemented for flood mapping at different segmentation scales. Finally, guided by the minimum uncertainty, an adaptive fusion strategy of multi-scale information is proposed to integrate the flood mapping results at different scales for producing the final flood map. The proposed approach is unsupervised, and can minimize the mapping uncertainty to improve mapping accuracy and reliability. These characteristics of the proposed approach make it practical. The results of comparative experiments demonstrate that the proposed approach is effective and has certain advantages over existing methods, especially in reducing false detections and correctly identifying the categories of uncertain pixels in flood mapping. Furthermore, the experimental results also indicate that the pseudo-color features constructed here also help enhance flood mapping accuracy.