Cloud-contaminated images seriously disturb the effective information in ground observations and reduce the availability of remote sensing images. This letter presents a thin cloud removal method for cloud-contaminated images based on a residual channel attention network. Compared with the present thin cloud removal methods not showing different attention to cloud components and ground details, the proposed method introduces channel attention mechanism into the residual learning path, which achieves suppressing thin clouds and enhancing the details of ground scenes simultaneously. Based on this, both residual channel attention module (RCAM) and residual group (RG) are constructed, playing the role of basic modules to stack an encoder-decoder-based residual channel attention network, which effectively avoids the loss of ground information in the deep layers. Compared with recent state-of-the-art (SOTA) methods, experimental results on both actual and synthesized cloudy images show the proposed method’s superiority in reconstructing clear high-frequency details.

Clouds are one of the most serious disturbances when using satellite imagery for ground observations. The semi-translucent nature of thin clouds provides the possibility of 2D ground scene reconstruction based on a single satellite image. In this paper, we propose an effective framework for thin cloud removal involving two aspects: a network architecture and a training strategy. For the network architecture, a Wasserstein generative adversarial network (WGAN) in YUV color space called YUV-GAN is proposed. Unlike most existing approaches in RGB color space, our method performs end-to-end thin cloud removal by learning luminance and chroma components independently, which is efficient at reducing the number of unrecoverable bright and dark pixels. To preserve more detailed features, the generator adopts a residual encoding–decoding network without down-sampling and up-sampling layers, which effectively competes with a residual discriminator, encouraging the accuracy of scene identification. For the training strategy, a transfer-learning-based method was applied. Instead of using either simulated or scarce real data to train the deep network, adequate simulated pairs were used to train the YUV-GAN at first. Then, pre-trained convolutional layers were optimized by real pairs to encourage the applicability of the model to real cloudy images. Qualitative and quantitative results on RICE1 and Sentinel-2A datasets confirmed that our YUV-GAN achieved state-of-the-art performance compared with other approaches. Additionally, our method combining the YUV-GAN with a transfer-learning-based training strategy led to better performance in the case of scarce training data.