RGAN: Rethinking generative adversarial networks for cloud removal

Abstract

Optical remote sensing imagery is at the core of many Earth observation activities. Many applications take use of the satellite data's regular, consistent, and global-scale characteristics, such as farmland monitoring, climate change assessment, land-cover, and land-use categorization, and catastrophe assessment. Optical remote sensing images, on the other hand, are frequently impacted by clouds during the collection process, resulting in reduced image clarity, which impairs feature assessment and future usage, and heavy cloud blockage renders the surface information below totally useless. In this paper, We propose a soft attention recurrent neural module based on an encoder-decoder network, which can solve the cloud occlusion problem. We also propose an adaptive padding convolution at the end of the decoder by taking into account the spatial information, which results in better declouding predictions, and our network achieves good results on the RICE1 and RICE2 data sets.