Generative Adversarial Learning in YUV Color Space for Thin Cloud Removal on Satellite Imagery

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.

Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery

Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., in Sentinel-2A/B, CBERS04, ZY-1 02D and HJ-1B satellites). Most cloud removal methods do not take advantage of the spectral information available in SWIR bands, which are less affected by clouds, to restore the background information tainted by thin clouds in Vis bands. In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR) bands, in order to improve cloud removal in Sentinel-2 visible bands. Contrary to some traditional and deep learning-based cloud removal methods, which use manually designed rescaling algorithm to handle bands at different resolutions, CR-MSS uses convolutional layers to automatically process bands at different resolution. CR-MSS has two input/output branches that are designed to process Vis/NIR and VRE/SWIR, respectively. Firstly, Vis/NIR cloudy bands are down-sampled by a convolutional layer to low spatial resolution features, which are then concatenated with the corresponding features extracted from VRE/SWIR bands. Secondly, the concatenated features are put into a fusion tunnel to down-sample and fuse the spectral information from Vis/NIR and VRE/SWIR bands. Third, a decomposition tunnel is designed to up-sample and decompose the fused features. Finally, a transpose convolutional layer is used to up-sample the feature maps to the resolution of input Vis/NIR bands. CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs. The average SSIM values (Structural Similarity Index Measurement) for CR-MSS results on Vis/NIR bands over all testing images were 0.69, 0.71, 0.77, and 0.81, respectively, which was on average 1.74% higher than the best baseline method. The visual results on real Sentinel-2 images demonstrate that CR-MSS can produce more realistic cloud and cloud shadow removal results than baseline methods.