DIEFEN: Differential Information-Enhanced Feature Exchange Network for Hyperspectral Change Detection

Hyperspectral image (HSI) change detection (CD) has gained significant attention in the field of remote sensing. Current CD methods typically extract features based on spatial or spectral correlations between bitemporal HSIs, which often overlook the difference information, leading to a decrease in CD accuracy. Furthermore, these algorithms do not fully consider the alignment of features between images across different channel and spatial dimensions. To tackle these issues, we propose a novel approach called the differential information-enhanced feature exchange network (DIEFEN) for HSI CD, which leverages the difference information between images and enhances the alignment of bitemporal image features to improve CD accuracy. Specifically, an enhanced differential multihead attention (EDMA) module is proposed to utilize difference information to guide the feature aggregation of bitemporal images, effectively highlighting changing pixels and suppressing unchanging pixels. A feature focus and long-range attention (FFLA) module is designed to extract local and global features, and a channel-spatial interaction (CSI) module is constructed to align features and mitigate the impact of noise. Experimental results on three HSI CD datasets demonstrate that the proposed DIEFEN method outperforms several state-of-the-art methods. The source code of the proposed DIEFEN is released at https://github.com/creativeXin/DIEFEN.