Cloud detection plays a major role for remote sensing image processing. Most of the existed cloud detection methods use the low-level feature of the cloud, which often cause error result especially for thin cloud and complex scene. In this paper, a novel cloud detection method based on deep learning framework is proposed. The designed deep Convolutional Neural Networks (CNNs) consists of four convolutional layers and two fully-connected layers, which can mine the deep features of cloud. The image is firstly clustered into superpixels as sub-region through simple linear iterative cluster (SLIC) method. Through the designed network model, the probability of each superpixel that belongs to cloud region is predicted, so that the cloud probability map of the image is generated. Lastly, the cloud region is obtained according to the gradient of the cloud map. Through the proposed method, both thin cloud and thick cloud can be detected well, and the result is insensitive to complex scene. Experimental results indicate that the proposed method is more robust and effective than compared methods.

Cloud detection for remote sensing images is often a necessary process, because cloud is widespread in optical remote sensing images and causes a lot of difficulty to many remote sensing activities, such as land cover monitoring, environmental monitoring and target recognizing. In this paper, a novel cloud detection method is proposed for multispectral remote sensing images from Landsat 8. Firstly, the color composite image of Bands 6, 3 and 2 is divided into superpixel sub-regions through Simple Linear Iterative Cluster (SLIC) method. Then, a two-step superpixel classification strategy is used to predict each superpixel as cloud or non-cloud. Thirdly, a fully connected Conditional Random Field (CRF) model is used to refine the cloud detection result, and accurate cloud borders are obtained. In the two-step superpixel classification strategy, the bright and thick cloud superpixels, as well as the obvious non-cloud superpixels, are firstly separated from potential cloud superpixels through a threshold function, which greatly speeds up the detection. The designed double-branch PCA Network (PCANet) architecture can extract the high-level information of cloud, then combined with a Support Vector Machine (SVM) classifier, the potential superpixels are correctly classified. Visual and quantitative comparison experiments are conducted on the Landsat 8 Cloud Cover Assessment (L8 CCA) dataset; the results indicate that our proposed method can accurately detect clouds under different conditions, which is more effective and robust than the compared state-of-the-art methods.

Multispectral remote sensing images are often degraded by clouds, resulting in the reduced efficiency and accuracy of image interpretation. Thin cloud removal is one of the most important and significant tasks for optical multispectral images. In this article, we propose a novel thin cloud removal method for multispectral images, which is a combination of traditional methods and deep learning methods. First, we adopt U-Net to estimate the reference thin cloud thickness map of the cloudy image. Then, a convolutional neural network named Slope-Net is designed to estimate the thickness coefficient of each band relative to the reference thin cloud thickness map to obtain the thin cloud thickness maps of different bands. Finally, the recovered clear image can be obtained by subtracting the thin cloud thickness maps from the cloudy image according to the traditional thin cloud imaging model. To train U-Net and Slope-Net, a wavelength-dependent thin cloud simulation method is presented to generate a labeled dataset composed of synthetic cloudy images, corresponding clear images, reference thin cloud thickness maps, and thickness coefficients. Qualitative and quantitative comparison experiments are conducted on both synthetic cloudy images and real cloudy images from the Landsat 8 Operational Land Imager. The results indicate that the proposed method can effectively remove thin clouds in multispectral images with various land cover types and maintain good color fidelity.

Thin cloud removal from remote sensing (RS) images is challenging. Recently, deep-learning-based methods have achieved excellent results using supervised training on paired image data. However, in practice, real paired image data are unavailable. Therefore, in this letter, we propose a novel thin cloud removal method, a physical-model-based CycleGAN (PM-CycleGAN), which can be trained using only unpaired data. The PM-CycleGAN training process comprises forward and backward loops. The forward loop first decomposes a cloudy image into a cloud-free image, thin cloud thickness map, and thickness coefficient using three generators. Then, it combines these three components using a physical model to reconstruct the original cloudy image to obtain the cycle consistency constraint. The backward loop first uses the physical model to synthesize a cloud-free image, thin cloud thickness map, and thickness coefficient into a cloudy image, which are then decomposed into the original three components using the three generators. Visual and quantitative comparisons against several state-of-the-art (SOTA) methods on a cloudy image dataset demonstrated the superiority of PM-CycleGAN.

Cloud occlusion phenomena are widespread in optical remote sensing (RS) images, leading to information loss and image degradation and causing difficulties in subsequent applications such as land surface classification, object detection, and land change monitoring. Therefore, thin cloud removal is a key preprocessing procedure for optical RS images, and has great practical value. Recent deep learning-based thin cloud removal methods have achieved excellent results. However, these methods have a common problem in that they cannot obtain large receptive fields while preserving image detail. In this paper, we propose a novel wavelet-integrated convolutional neural network for thin cloud removal (WaveCNN-CR) in RS images that can obtain larger receptive fields without any information loss. WaveCNN-CR generates cloud-free images in an end-to-end manner based on an encoder–decoder-like architecture. In the encoding stage, WaveCNN-CR first extracts multi-scale and multi-frequency components via wavelet transform, then further performs feature extraction for each high-frequency component at different scales by multiple enhanced feature extraction modules (EFEM) separately. In the decoding stage, WaveCNN-CR recursively concatenates the processed low-frequency and high-frequency components at each scale, feeds them into EFEMs for feature extraction, then reconstructs the high-resolution low-frequency component by inverse wavelet transform. In addition, the designed EFEM consisting of an attentive residual block (ARB) and gated residual block (GRB) is used to emphasize the more informative features. ARB and GRB enhance features from the perspective of global and local context, respectively. Extensive experiments on the T-CLOUD, RICE1, and WHUS2-CR datasets demonstrate that our WaveCNN-CR significantly outperforms existing state-of-the-art methods.