A Physics-Guided Illumination Compensation Framework for Shadow Removal in Remote Sensing Images

Shadows in high-resolution urban remote sensing imagery significantly degrade radiometric and structural information, thereby limiting the performance of downstream tasks such as classification and object extraction. Therefore, effective shadow removal is essential for improving the reliability of urban remote sensing applications. Existing methods still exhibit limitations in accurately detecting complex shadows, especially small-scale shadows and ambiguous boundaries, and shadow compensation in umbra regions often suffers from under-correction due to inadequate illumination modeling. To address these challenges, a physics-guided shadow removal framework that integrates lightweight shadow detection with illumination-aware compensation is proposed. A lightweight U-Net (LSDU) is designed to efficiently capture multi-scale shadow features, while a modified illumination intensity ratio method (MIIRM) is developed to explicitly model illumination differences between umbra and penumbra. Furthermore, a dynamic penumbra compensation method (MDPCM) is introduced to alleviate over-compensation effects in transition regions and improve radiometric consistency. Experiments on the Aerial Imagery Shadow Dataset (AISD) demonstrate that the proposed method achieves over 96% overall accuracy in shadow detection and the lowest RMSE in shadow compensation among existing state-of-the-art methods, while maintaining strong robustness across diverse urban scenes.