Residual Group Enhanced GAN for Remote Sensing Image Cloud Shadow Removal

Jiajun Yang, Chenyang Liu, Keyan Chen, Zhengxia Zou, Zhenwei Shi

Abstract: Cloud shadow is an unignorable factor affecting the quality of remote sensing images, but there are few researches specifically focusing on cloud shadow removal and its impact on downstream remote sensing tasks. In this paper, we propose a residual group enhanced generative adversarial network (RGE-GAN) for cloud shadow removal. We design an encoder-decoder with residual group enhancement (RGE) module to remove cloud shadows from remote sensing images. RGE module can effectively enhance the deep features extracted by encoder. We further introduce a discriminator network and employ adversarial training strategy to constrain the generator to reconstruct high-quality cloud shadow removed images conforming to the distribution of remote sensing images. The joint experiments of cloud shadow removal and building extraction on real remote sensing dataset show that our cloud shadow removal method can effectively enhance the quality of remote sensing images and improve the performance of downstream remote sensing processing tasks.