Thin Cloud Removal for Remote Sensing Images Using a Physical-Model-Based CycleGAN With Unpaired Data

Abstract

Thin cloud removal from remote sensing (RS) images is challenging. Recently, deep-learning-based methods have achieved excellent results using supervised training on paired image data. However, in practice, real paired image data are unavailable. Therefore, in this letter, we propose a novel thin cloud removal method, a physical-model-based CycleGAN (PM-CycleGAN), which can be trained using only unpaired data. The PM-CycleGAN training process comprises forward and backward loops. The forward loop first decomposes a cloudy image into a cloud-free image, thin cloud thickness map, and thickness coefficient using three generators. Then, it combines these three components using a physical model to reconstruct the original cloudy image to obtain the cycle consistency constraint. The backward loop first uses the physical model to synthesize a cloud-free image, thin cloud thickness map, and thickness coefficient into a cloudy image, which are then decomposed into the original three components using the three generators. Visual and quantitative comparisons against several state-of-the-art (SOTA) methods on a cloudy image dataset demonstrated the superiority of PM-CycleGAN.