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• TC-BC can remove and detect thin cloud simultaneously. • By coupling the cloudy imaging model and unsupervised deep learning method, TC-BC reduce the cost of dataset construct. • The feature separation network (FSNet) in TC-BC is demonstrated better than existing networks. 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. [ABSTRACT FROM AUTHOR]

Optical satellite images frequently suffer from thin clouds, degrading the data quality. A thin cloud correction method is developed based on complementary dark objects on multiple visible bands to address this problem. First, thin cloud images are divided into irregular subareas using the superpixel segmentation algorithm, enabling the proper identification of dark objects in the spatial domain across different visible bands. A criterion is then established to classify the dark objects into two types, namely, absolute dark objects (ADOs) and relative dark objects (RDOs). Subsequently, the quantitative correlation of thin clouds between visible bands is estimated by adopting the ADOs. Dark objects present complementarity in visible bands; thus, the RDOs on one band are spatially densified by referencing the RDOs on the other visible bands. Thereby, a thin cloud map with fine spatial details is interpolated by using all the ADOs and RDOs on a band, and the correction procedure is performed through subtraction. Eight visible data captured by Landsat platforms are collected for simulated and real experiments to evaluate the method's performance. Three representative thin cloud correction approaches are selected for visual and quantitative comparisons. The proposed method can correct thin clouds effectively and restore various scenes accurately. The interpolated thin cloud maps show enhanced texture details and finer representation compared with the benchmarks. In addition, the advantages of dark-object densification for thin cloud map generation and the limitations of the proposed method are investigated.

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 (

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