Flood Change Detection Based on Prior Feature Estimation

Flood caused by torrential rain is one of the most influential meteorological disasters in the world. Currently, most of the flood change detection networks are based on homogeneous images. However, due to the influence of bad weather and satellite revisit cycle, the acquisition of homogeneous images is greatly limited. In this paper, a novel heterogeneous image change detection network based on prior feature estimation is proposed. In order to better guide the network to find the solution space related to change, we propose feature enhancement module to strengthen the water body features and introduce auxiliary information. At the same time, the fusion module is designed to solve the problem that the feature space of heterogeneous images is difficult to align while mapping water features into changing features. Experimental results on the CAU-Flood dataset demonstrate the effectiveness of our network.