Wavelet Siamese Network With Semi-Supervised Domain Adaptation for Remote Sensing Image Change Detection

Fengchao Xiong, Tianhan Li, Yi Yang, Jun Zhou, Jianfeng Lu, Yuntao Qian

Abstract: Change detection is a crucial technique in remote sensing image analysis and faces challenges, such as background complexity and appearance shift, resulting in incomplete change boundaries and pseudochanges. This article introduces a novel wavelet Siamese network with semi-supervised domain adaptation (DA) to address these issues, named WS-Net++. WS-Net++ establishes spatial–frequency interactions between bitemporal images to enhance the completeness of the change boundaries. The spatial-domain interaction highlights the pixelwise differences. The frequency-domain interaction first adaptively adjusts the contributions from different frequency components based on image context. Within-frequency and between-frequency interactions are further constructed to capture the frequency-domain differences, enabling the adaptive and effective handling of both overall and subtle changes. In addition, WS-Net++ employs a semi-supervised DA strategy to mitigate the appearance shifts between bitemporal images. By categorizing regions into changed, unchanged, and regions of no interest in a semi-supervised manner, the network minimizes intraclass discrepancies within unchanged regions and maximizes interclass discrepancies between changed regions, reducing the domain gap. Experimental results on the LEVIR-CD, WHU-CD, and CLCD datasets demonstrate that our WS-Net++ outperforms alternative methods, achieving the $F1$ scores of 91.31%, 94.52%, and 79.77%, respectively. The code and models will be publicly available at https://github.com/JiTaiTai/WS-Net_Plus for reproducible research.