A Siamese Multiscale Attention Decoding Network for Building Change Detection on High-Resolution Remote Sensing Images

The objective of building change detection (BCD) is to discern alterations in building surfaces using bitemporal images. The superior performance and robustness of various contemporary models suggest that rapid development of BCD in the deep learning age is being witnessed. However, challenges abound, particularly due to the diverse nature of targets in urban settings, intricate city backgrounds, and the presence of obstructions, such as trees and shadows, when using very high-resolution (VHR) remote sensing images. To overcome the shortcomings of information loss and lack of feature extraction ability, this paper introduces a Siamese Multiscale Attention Decoding Network (SMADNet). This network employs the Multiscale Context Feature Fusion Module (MCFFM) to amalgamate contextual information drawn from multiscale target, weakening the heterogeneity between raw image features and difference features. Additionally, our method integrates a Dual Contextual Attention Decoding Module (CADM) to identify spatial and channel relations amongst features. For enhanced accuracy, a Deep Supervision (DS) strategy is deployed to enhance the ability to extract more features for middle layers. Comprehensive experiments on three benchmark datasets, i.e., GDSCD, LEVIR-CD, and HRCUS-CD, establish the superiority of SMADNet over seven other state-of-the-art (SOTA) algorithms.