Hybrid GAN and Spectral Angular Distance for Cloud Removal

Abstract

This paper aims to present a new algorithm to remove thin clouds and retain information in corrupted images without the use of auxiliary data. By injecting physical properties into the cycle consistent generative adversarial network (GAN), we were able to convert a cloudy multispectral image to a cloudless image. To recover information beneath clouds and shadows we create a synthetic multispectral space to obtain illumination invariant features. Multispectral vectors were transformed from Cartesian coordinates to Polar coordinates to obtain spectral angular distance (SAD) then we employed them as input to train the deep neural network (DNN). Afterward, the outputs of DNN were transformed to Cartesian coordinates to obtain shadow and cloud-free multispectral images. The proposed method, Hybrid GAN-SAD yields trustworthy reconstructed results because of exploiting transparent information from certain multispectral bands to recover uncorrupted images.