A Cross-Hierarchical Difference Feature Fusion Network Based on Multiscale Encoder–Decoder for Coastal Wetlands Hyperspectral Change Detection

Hyperspectral Change Detection (HCD) is one of the core applications of remote sensing imagery, and it holds profound significance and research value for monitoring the dynamic changes of coastal wetlands with extremely high ecological value. However, existing methods often fail to fully capture the multiscale spatial-spectral features of coastal wetland changes, and they inadequately fuse differential feature information. To address these issues, this paper proposes a Cross-Hierarchical Difference Feature Fusion Network (CHDFFN) based on a multiscale encoder-decoder. Taking a customized encoder-decoder as its backbone, this network integrates a multiscale feature extraction subnetwork with residual connections and a dual-kernel channel-spatial attention module, enabling the multi-level extraction and initial fusion of spatial-spectral features of coastal wetlands. The encoder uses convolutional blocks with varying receptive fields to capture multiscale representations ranging from shallow details to deep semantics; while the decoder fuses output results via skip connections to restore spatial resolution and suppress noise. In addition, the spatial-spectral change feature learning module is used to learn hierarchical change representations, and the adaptive high-level feature fusion module dynamically balances the contributions of hierarchical differential features through adaptive weight assignment—enhancing the model's ability to characterize the complex changes of coastal wetlands. Finally, experimental results on four public datasets show that: compared with state-of-the-art methods, this model achieves an average maximum improvement of 4.61% in Overall Accuracy, 19.79% in the Kappa Coefficient, and 18.90% in the F1-score, verifying its effectiveness in coastal wetland change detection. In the field of remote sensing, accurate and reliable spectral-spatial change detection is crucial for coastal wetland monitoring and environmental management. The source code of this study will be available athttps://github.com/SMingshuai/CHDFFN.