Recreating Brightness From Remote Sensing Shadow Appearance

Shadow removal from remote sensing images is still an open issue. Recently, deep network training on unpaired data is preferable since corresponding ground truths of shadow images are not available in practice. Nevertheless, unsupervised shadow removal research for remote sensing imagery is limited by the scarcity of publicly available benchmarks. This article proposes an unsupervised progressive network (UP-ShadowGAN) to jointly learn decoupled features for shadow removal and color transfer. UP-ShadowGAN explores the mapping between shadow and shadow-free domains through adversarial learning and cycle consistency constraint. In particular, we employ progressive learning to decompose the overall mapping process into more manageable shadow removal and color transfer steps. Specifically, the realistic illumination is restored by propagating spatial context between shadow and shadow-free nodes. Coupled with a multicolor space aggregation strategy, diverse color space representations alleviate color deviation caused by spatial inconsistency. More importantly, we contribute the first unpaired remote sensing shadow removal (URSSR) dataset, which encourages future exploration. Extensive experiments demonstrate that UP-ShadowGAN competes favorably with state-of-the-art methods. The dataset and code are available at https://github.com/chi-kaichen/UP-ShadowGAN.