Shadow detection and removal for remote sensing images via multi-feature adaptive optimization and geometry-aware illumination compensation

Abstract

Shadows in remote sensing images degrade quality and obscure ground details, posing challenges in their accurate detection and removal. The biggest challenge in shadow removal is accurately detecting the shadow while restoring normal illumination. Therefore, this paper proposes a novel approach combining multi-feature adaptive optimization and geometry-aware illumination compensation for shadow detection and removal. The method introduces a novel multi-feature adaptive optimization algorithm, which simulates dynamic interaction behavior of snakes to obtain optimal shadow thresholds from multi-feature channels, achieving precise shadow detection. Then, Sunlit regions homogeneous to shadows are identified through irregular block matching, utilizing direction-adaptive feature extraction. Finally, we deduct geometry-aware illumination compensation theoretically to effectively remove shadows and restore normal lighting. Additionally, at the shadow boundaries, a Manhattan-based dynamic compensation method is designed to ensure smooth boundary transitions and mitigate pixel oversaturation. Experimental results demonstrate the superior performance of the proposed method compared to state-of-the-art methods of shadow detection and removal in both qualitative and quantitative ways. Overall, the proposed method provides a promising solution to the challenging problem of shadow in remote-sensing images. The code will be available at https://github.com/whuzzzz/MAOSD.