Interactive Siamese spatial-Spectral cross-layer fusion transformer for hyperspectral image change detection

Recently, methods based on Transformer have been widely used in the research field of hyperspectral image (HSI) change detection (CD). However, existing transformer-based CD research does not sufficiently utilize the spatial-spectral features of HSIs. In this article, we propose an interactive Siamese spatial-spectral cross-layer fusion Transformer (IS2CF-Former) network to improve the accuracy of HSI-CD. The proposed Siamese interactive module integrates the Siamese network with the Transformer structure, enhancing communication between bi-temporal images. We have made improvements to the cross-layer adaptive fusion (CAF) Transformer, where the cross-layer fusion module enhances the interaction between layers and the ability to capture local contextual features, concurrently reducing the model's parameter count to mitigate the risk of overfitting. The CAF Transformer is applied to extract spatial and spectral features. Evaluating the detection performance of the proposed model on three bi-temporal HSIs through extensive experiments demonstrates superior accuracy compared to seven excellent CD methods.