GCCD: A Generative Cross-Domain Change Detection Network

Change detection (CD) in hyperspectral image (HSI) is of great importance in the remote sensing area. The HSI-CD method based on deep learning (DL) has shown significant progress in achieving precise detection performance. However, many existing methods overlook cross-domain challenges in the CD task. In addition, the scarcity of annotated samples makes the DL models prone to overfitting. To address these issues, a generative cross-domain CD (GCCD) network based on a domain generalization (DG) technique is proposed. GCCD consists of a generator and a discriminator. The generator, with a Morph encoder (ME) and a Semantic encoder (SE), preserves fundamental structural information while introducing randomization to style and content. The discriminator extracts change information for discrimination through dual-temporal images and their difference map. Supervised adversarial learning between the generator and discriminator enhances the model's ability to extract domain-invariant information. Extensive experiments on various datasets demonstrate the superior performance of the proposed method.