Damaged Building Detection From Post-Earthquake Remote Sensing Imagery Considering Heterogeneity Characteristics

Abstract

Damaged building detection from remote sensing imagery helps to quickly and rapidly assess losses after an earthquake. In recent years, deep learning technology has become a favorable tool for remote sensing image information detection. Based on the characteristics of damaged buildings in remote sensing images, in this paper, a framework for damaged building detection that considers heterogeneity characteristics is proposed. First, a local-global context attention module is proposed to improve the feature detection ability of the network, which can extract the features of damaged buildings from different directions and effectively aggregate global and local features. In addition, the module takes the correlation between feature maps at different scales into account while extracting information. Second, a feature fusion module with self-attention is established to replace the simple connection between the encoding and decoding processes, which improves the detail feature recovery ability of the network during the upsampling process. Finally, to fully aggregate semantic and detail features at different scales, a multibranch auxiliary classifier is established by adding two separate branches in the prediction stage. The effectiveness of the proposed approach is verified based on data from the 2010 Haiti earthquake, and comparisons with 3 object-oriented methods and 16 existing excellent deep learning models are performed. The IOU increase of 0.03%-7.39% is achieved using the proposed approach compared with excellent deep learning models.