CTACL:Hyperspectral Image Change Detection Based on Adaptive Contrastive Learning

Hyperspectral image change detection (HSI-CD) can accurately identify changing regions by capturing subtle spectral differences and has become a research hotspot in the field of remote sensing (RS). Convolutional neural networks (CNNs) have excellent local context modeling capabilities and have been proven to be powerful feature extractors in HSI-CD. However, due to its inherent network structure limitation, CNN cannot well mine and represent the sequential properties of spectral features, especially the medium and long-term dependencies. In contrast, transformer-based network architecture shows a strong ability to model long-distance dependencies, which can fully mine and extract global features, but exhibits weak performance in extracting local information. To this end, we propose HSI-CD network based on adaptive contrastive learning (CTACL). Specifically, we first propose a parallel network of CNNs and transformers to mine local and global temporal-spatial-spectral features of HSI, respectively. Second, we propose adaptive contrastive learning to pre-train the network to learn the latent features of a large amount of unlabeled data and better mine and utilize local and global information. Experimental results on the farmland dataset show that the proposed method performs well.