LDGNet: A Lightweight Difference Guiding Network for Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2504.05062v2
• Date: Tue, 15 Apr 2025 08:15:27 GMT
• Title: LDGNet: A Lightweight Difference Guiding Network for Remote Sensing Change Detection
• Authors: Chenfeng Xu,
• Abstract summary: We propose a Lightweight Difference Guiding Network (LDGNet) to guide optical remote sensing change detection.<n>First, to enhance the feature representation capability of the lightweight backbone network, we propose the Difference Guiding Module (DGM)<n>Second, we propose the Difference-Aware Dynamic Fusion (DADF) module with Visual State Space Model (VSSM) for lightweight long-range dependency modeling.
• Score: 6.554696547472252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advancement of deep learning, the field of change detection (CD) in remote sensing imagery has achieved remarkable progress. Existing change detection methods primarily focus on achieving higher accuracy with increased computational costs and parameter sizes, leaving development of lightweight methods for rapid real-world processing an underexplored challenge. To address this challenge, we propose a Lightweight Difference Guiding Network (LDGNet), leveraging absolute difference image to guide optical remote sensing change detection. First, to enhance the feature representation capability of the lightweight backbone network, we propose the Difference Guiding Module (DGM), which leverages multi-scale features extracted from the absolute difference image to progressively influence the original image encoder at each layer, thereby reinforcing feature extraction. Second, we propose the Difference-Aware Dynamic Fusion (DADF) module with Visual State Space Model (VSSM) for lightweight long-range dependency modeling. The module first uses feature absolute differences to guide VSSM's global contextual modeling of change regions, then employs difference attention to dynamically fuse these long-range features with feature differences, enhancing change semantics while suppressing noise and background. Extensive experiments on multiple datasets demonstrate that our method achieves comparable or superior performance to current state-of-the-art (SOTA) methods requiring several times more computation, while maintaining only 3.43M parameters and 1.12G FLOPs.

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