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Change detection has always played a crucial role in observing the Earth's surface. Although visual Transformers (ViTs) have achieved excellent performance in change detection, they require high computational complexity. In addition, it does not explicitly address the differences caused by factors such as climate, lighting, and shooting angle in remote sensing (RS) image pairs. Therefore, we propose an efficient, lightweight Siamese network that considers multi-dimensional feature interaction. Firstly, we use a lightweight Transformer-based headless backbone network to extract feature information at each stage for bi-temporal images. To better capture the details and structure of images and eliminate the adverse effects of climate, lighting, and shooting angle, we design a multidimensional feature interaction method that uses spatial, channel, and amplitude dimension interaction methods after feature extraction operations at different stages. Furthermore, this approach achieves domain adaptation between bi-temporal domains to a certain extent while preserving the original semantic correspondence. Comprehensive experiments and extensive ablation studies on two common datasets, LEVIR-CD and S2Looking, have shown that our method achieves better performance with fewer parameters.
Zhang et al. (Mon,) studied this question.