Abstract. Deep-learning methods dominate remote-sensing change detection (CD), yet state-of-the-art models remain parameter-heavy and struggle with crisp boundaries, limiting their use on edge devices. We present LEDGNet, a Lightweight, Edge-knowledge- Distillation-Guided CD Network, that reconciles accuracy, boundary fidelity, and efficiency. LEDGNet integrates three purpose-built components: 1) an Edge Distillation Module that mines multi-scale boundary cues from a high-capacity teacher and transfers them to a compact student through an edge-aware loss; 2) StarLite, a depth-wise separable encoder that preserves fine spatial detail while minimizing floating-point operations; and 3) LiteDecoder, an inexpensive feature-fusion head that restores full resolution without bulky up-sampling. This design halves the parameters and inference time of mainstream fine-grained CD networks while enhancing edge sharpness. On the CDD and LEVIR-CD benchmarks, LEDGNet achieves competitive F1 performance while maintaining a compact footprint of 20.58 M parameters and 35.18 G FLOPs. With an inference time of 255 ms, it strikes a balance between resource consumption and detection efficiency, making it well-suited for high-efficiency remote sensing monitoring.
Tingyu Ji, Yixin Chen, Ruiqian Zhang et al.· ISPRS Annals of the Photogra...· 0 citations
Remote sensing image super-resolution is a critical task for reconstructing high-fidelity images from low-resolution observations. However, practical remote sensing scenarios often involve complex, compound degradations that severely compromise essential high-frequency structures, such as sharp edges and fine textures. While conventional convolutional neural network and Transformer-based methods have shown promise, they predominantly rely on static feature representations that lack adaptability to diverse scenes and often struggle to restore specific high-frequency details due to spectral bias. To this end, we propose a novel frequency-aware prompt learning framework. Specifically, a frequency-aware transformer module is designed to leverage the fast Fourier transform for explicit high-frequency component restoration, thereby overcoming the frequency bias inherent in spatial-domain models and effectively recovering intricate structural details. Concurrently, a dynamically generated prompt modulator is introduced to provide scene-specific adaptability via learnable vectors. This allows the network to adaptively calibrate feature responses to mitigate diverse environmental variations and compound artifacts. This synergistic integration ensures superior reconstruction fidelity and robust generalization across heterogeneous remote sensing scenarios. Comprehensive experiments across three benchmark datasets demonstrate that our method achieves outstanding performance in both quantitative metrics and visual quality assessments.
Kangli Zeng, Hang Hu, Ying Yu et al.· IEEE Journal of Selected Top...· 0 citations
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