Abstract:The accurate onboard detection of clouds in hyperspectral images before lossless compression is beneficial. However, conventional onboard cloud detection methods are not applicable all the time, especially for shadowed clouds or darkened snow-covered surfaces that are not identified in normalized difference snow index (NDSI) tests. In this paper, we propose a new spectral-spatial classification strategy to enhance the performance of an orbiting cloud screen obtained on hyperspectral images by integrating a threshold exponential spectral angle map (TESAM), adaptive Markov random field (aMRF) and dynamic stochastic resonance (DSR). TESAM is applied to roughly classify cloud pixels based on spectral information. Then aMRF is used to do optimal process by using spatial information, which improved the classification performance significantly. Nevertheless, misclassifications occur due to noisy data in the onboard environments, and DSR is employed to eliminate noise data produced by aMRF in binary labeled images. We used level 0.5 data from Hyperion as a dataset, and the average tested accuracy of the proposed algorithm was 96.28% by test. This method can provide cloud mask for the on-going EO-1 and related satellites with the same spectral settings without manual intervention. Experiments indicate that the proposed method has better performance than the conventional onboard cloud detection methods or current state-of-the-art hyperspectral classification methods.
Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.
Cloud pixels have massively reduced the utilization of optical remote sensing images, highlighting the importance of cloud detection. According to the current remote sensing literature, methods such as the threshold method, statistical method and deep learning (DL) have been applied in cloud detection tasks. As some cloud areas are translucent, areas blurred by these clouds still retain some ground feature information, which blurs the spectral or spatial characteristics of these areas, leading to difficulty in accurate detection of cloud areas by existing methods. To solve the problem, this study presents a cloud detection method based on genetic reinforcement learning. Firstly, the factors that directly affect the classification of pixels in remote sensing images are analyzed, and the concept of pixel environmental state (PES) is proposed. Then, PES information and the algorithm’s marking action are integrated into the “PES-action” data set. Subsequently, the rule of “reward–penalty” is introduced and the “PES-action” strategy with the highest cumulative return is learned by a genetic algorithm (GA). Clouds can be detected accurately through the learned “PES-action” strategy. By virtue of the strong adaptability of reinforcement learning (RL) to the environment and the global optimization ability of the GA, cloud regions are detected accurately. In the experiment, multi-spectral remote sensing images of SuperView-1 were collected to build the data set, which was finally accurately detected. The overall accuracy (OA) of the proposed method on the test set reached 97.15%, and satisfactory cloud masks were obtained. Compared with the best DL method disclosed and the random forest (RF) method, the proposed method is superior in precision, recall, false positive rate (FPR) and OA for the detection of clouds. This study aims to improve the detection of cloud regions, providing a reference for researchers interested in cloud detection of remote sensing images.
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