KO-Shadow: KnOwledge-Driven Shadow Progressive Removal Framework for Very High Spatial Resolution Remote Sensing Imagery

The formation of shadows in very high spatial resolution (VHR) remote sensing imagery is attributed to light being blocked by objects, reducing spectral radiance in the shadow landscape. An accurate and robust shadow removal method can recover spectral and textural information and, hence, is a crucial preprocessing step for urban image analyses. In this study, we develop a KnOwledge-driven shadow progressive removal (KO-Shadow) framework with three subnets for VHR imagery using a weakly supervised manner. Specifically, the shadow preelimination subnet is proposed to initially address the large chromatic aberration between the real and shadow situations. Then, the prior knowledge-guided refinement subnet is proposed to refine the preelimination results by mining tone and texture information. Moreover, the locality feature discriminator is designed for region-specific evaluation of the generated shadow-free samples to improve the capacity of subnets. Experimental results of six typical cities in the world show that KO-Shadow is superior to the existing methods. Moreover, the generalizability analysis in complex urban scenarios validates the robustness of our method. The shadow recovery score (SRI) is proposed to evaluate the spectral similarities between the recovered area and shadow-related land-cover types (e.g., road, building, and lawn). The results show that KO-Shadow can yield more visually realistic shadow-free images and better quantitative performance. Overall, KO-Shadow provides a new perspective for VHR image shadow removal by mining the prior knowledge of the complex shadows in urban areas.