A thin cloud blind correction method coupling a physical model with unsupervised deep learning for remote sensing imagery

Abstract

Thin cloud disturbs the observation of optical sensors, thus reducing the quality of optical remote sensing images and limiting the subsequent applications. However, the reliance of the existing thin cloud correction methods on the assistance of in-situ parameters, prior assumptions, massive paired data, or special bands severely limits their generalization. Moreover, due to the inadequate consideration of cloud characteristics, these methods struggle to obtain accurate results with complex degradations. To address the above two problems, a thin cloud blind correction (TC-BC) method coupling a cloudy image imaging model and a feature separation network (FSNet) module is proposed in this paper, based on an unsupervised self-training framework. Specifically, the FSNet module takes the independence and obscure boundary characteristics of the cloud into account to improve the correction accuracy with complex degradations. The FSNet module consists of an information interaction structure for exchanging the complementary features between cloud and ground, and a spatially adaptive structure for promoting the learning of the thin cloud distribution. Thin cloud correction experiments were conducted on an unpaired blind correction dataset (UBCSet) and the proposed TC-BC method was compared with three traditional methods. The visual results suggest that the proposed method shows obvious advantages in information recovery for thin cloud cover regions, and shows a superior global consistency between cloudy regions and clear regions. The TC-BC method also achieves the highest peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The FSNet module in the TC-BC method is also proven to be effective. The FSNet module can achieve a superior precision when compared with five other deep learning networks in cloud-ground separation performance. Finally, extra experimental results show that the TC-BC method can be applied to different cloud correction scenarios with varied cloud coverage, surface types, and image scales, demonstrating its generalizability. Code: https://github.com/Liying-Xu/TCBC.