{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:29:28Z","timestamp":1765232968011,"version":"build-2065373602"},"reference-count":65,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T00:00:00Z","timestamp":1601337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019s marking action are integrated into the \u201cPES-action\u201d data set. Subsequently, the rule of \u201creward\u2013penalty\u201d is introduced and the \u201cPES-action\u201d strategy with the highest cumulative return is learned by a genetic algorithm (GA). Clouds can be detected accurately through the learned \u201cPES-action\u201d 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.<\/jats:p>","DOI":"10.3390\/rs12193190","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T20:56:22Z","timestamp":1601412982000},"page":"3190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Cloud Detection of SuperView-1 Remote Sensing Images Based on Genetic Reinforcement Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Xiaolong","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Beijing 100191, China"}]},{"given":"Hong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Beijing 100191, China"}]},{"given":"Chuanzhao","family":"Han","sequence":"additional","affiliation":[{"name":"China Academy of Space Technology (CAST), Beijing 100094, China"}]},{"given":"Haibo","family":"Wang","sequence":"additional","affiliation":[{"name":"China Centre for Resources Satellite Data and Application, No. 5 Fengxian East Road, Beijing 100094, China"}]},{"given":"Kaihan","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Beijing 100191, China"}]},{"given":"Ying","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Beijing 100191, China"}]},{"given":"Wentao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, H., Zheng, H., Han, C., Wang, H., and Miao, M. 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