An Improved Method for Removal of Thin Clouds in Remote Sensing Images by Generative Adversarial Network

Abstract

Thin clouds will attenuate surface radiation, causing color distortion in remote sensing images and loss of detailed information. Aiming at the color cast phenomenon of the existing thin cloud removal methods, this paper proposes a remote sensing image thin cloud removal method based on generative adversarial networks with color consistency constraints. The RICE1 thin cloud dataset was used to verify the effectiveness of the method. CIEDE2000 was added to the evaluation index to measure the consistency of the thin cloud removal results and the color of the cloud-free image, and the reasonable value of the color constraint parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> was explored. The experimental results show that the method in this paper is significantly better than homomorphic filtering, dark channel prior method and non-color-constrained generative adversarial network cloud removal method.