{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T07:31:05Z","timestamp":1772177465644,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"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>Due to the limited penetration of visible bands, optical remote sensing images are inevitably contaminated by clouds. Therefore, cloud detection or cloud mask products for optical image processing is a very important step. Compared with conventional optical remote sensing satellites (such as Landsat series and Sentinel-2), sustainable development science Satellite-1 (SDGSAT-1) multi-spectral imager (MII) lacks a short-wave infrared (SWIR) band that can be used to effectively distinguish cloud and snow. To solve the above problems, a cloud detection method based on spectral and gradient features (SGF) for SDGSAT-1 multispectral images is proposed in this paper. According to the differences in spectral features between cloud and other ground objects, the method combines four features, namely, brightness, normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and haze-optimized transformation (HOT) to distinguish cloud and most ground objects. Meanwhile, in order to adapt to different environments, the dynamic threshold using Otsu\u2019s method is adopted. In addition, it is worth mentioning that gradient features are used to distinguish cloud and snow in this paper. With the test of SDGSAT-1 multispectral images and comparison experiments, the results show that SGF has excellent performance. The overall accuracy of images with snow surface can reach 90.80%, and the overall accuracy of images with other surfaces is above 94%.<\/jats:p>","DOI":"10.3390\/rs15010024","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T02:06:14Z","timestamp":1671674774000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Cloud Detection Method Based on Spectral and Gradient Features for SDGSAT-1 Multispectral Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Kaiqiang","family":"Ge","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayin","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6494-3639","authenticated-orcid":false,"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Hu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106565","DOI":"10.1016\/j.asoc.2020.106565","article-title":"Monitoring Agriculture Areas with Satellite Images and Deep Learning","volume":"95","author":"Nguyen","year":"2020","journal-title":"Appl. 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