SFIEET: Spectral Frequency-Induced Edge Enhancement Transformer for Hyperspectral Change Detection

Deep learning methods have achieved remarkable performance in hyperspectral image (HSI) change detection. However, they still face two primary limitations. First, most approaches treat all spectral channels equally, overlooking the varying importance of spectral information. Second, they often struggle to clearly delineate edges in low-resolution images, resulting in suboptimal feature extraction and increased false or missed detections. To address these challenges, we propose a novel Spectral Frequency-Induced Edge Enhancement Transformer (SFIEET) network to enhance the accuracy and robustness of HSI change detection. The proposed SFIEET framework comprises three key modules. The Spectral Frequency and Spatial Domain Fusion (SFSDF) module implements an adaptive frequency-domain attention mechanism based on grouped DCT, dynamically focusing on channels rich in weak semantic information. Simultaneously, it integrates multi-scale contextual spatial information to capture morphological changes such as texture fragmentation, effectively enhancing recognition of structural changes. The Sharpening and Laplace-Guided Edge Enhancement (SLGEE) module integrates multi-scale pooling with the Laplacian operator to fuse edge features across spectral bands, enabling precise boundary localization and more reliable detection in blurred regions. Finally, the Depthwise Convolution-Embedded Lightweight Transformer (DW-ELT) module incorporates depthwise separable convolutions into the Transformer architecture to enhance local feature modeling, while gated linear units (GLUs) strengthen nonlinear representations, making change-region features more discriminative. Extensive experiments on three benchmark datasets demonstrate that the proposed SFIEET consistently outperforms existing state-of-the-art methods. The source code is available at https://github.com/bcshi83/SFIEET.git for the sake of reproducibility.