From Synthesis to Removal: A Deep Learning-Based Framework for Shadow Removal in High-Resolution Remote Sensing Images

Shadow removal is beneficial for various remote sensing applications, such as semantic segmentation and object detection. However, traditional shadow removal methods perform poorly when applied to high-resolution remote sensing images. Some deep learning-based models exhibit high-level accuracy of removal, but it is difficult to be expanded to multi-scenarios due to insufficient pairs of shadow/shadow-free data in reality. In this paper, a novel framework is proposed from shadow synthesis to shadow removal, aiming to improve the usability of deep learning-based models in the task of shadow removal in high-resolution remote sensing images. First, we combine the physical shadow illumination model and a domain alignment network to synthesize a large variety of realistic shadows. Then, a shadow removal network considering global-local features is built to restore the ground surface details finely. Numerous experiments have shown that the proposed framework can effectively remove various shadows and is superior to existing methods.