Thin cloud correction method for visible remote sensing images using a spectral transformation scheme

Thin cloud contamination adversely influences the interpretation of ground surface information in optical remote sensing images, especially for visible spectral bands with short wavelengths. To eliminate the thin cloud effect in visible remote sensing images, this study proposes a thin cloud correction method based on a spectral transformation scheme. Given the strong linear correlation among the three visible bands, a spectral transformation scheme is presented to derive intermediate images and further estimate spectrally varied transmission maps for the three visible bands. An improved strategy that locally estimates atmospheric light was also developed to produce spatially varied atmospheric light maps. Spectrally varied transmission maps and spatially varied atmospheric light maps contribute to the complete removal of thin clouds. Several remotely sensed images featuring various land covers were collected from Landsat 8 Operational Land Imager, Landsat 7 Enhanced Thematic Mapper Plus, and GaoFen-2 to perform simulated and real tests and verify the effectiveness and universality of the presented approach. Four existing thin cloud correction algorithms were used as baseline for comprehensive evaluation, including three single-image-based approaches and a deep-learning-based approach. Results demonstrate that the proposed method outperformed the other four baseline methods, yielding more natural and clearer cloud-free images. The ground surface information in the cloud-contaminated images can be appropriately recovered by the proposed method, with high determination coefficients (>0.8906) and low root-mean-square errors (<2.4711) and spectral angle maps (<0.8870). In summary, the proposed method can completely remove thin clouds, faithfully recover ground surface information, and effectively work for visible remote sensing images from diverse sensors. Furthermore, the factors potentially affecting correction performance and the applicability to open-source datasets were investigated.