{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:49:32Z","timestamp":1760237372078,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T00:00:00Z","timestamp":1589155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Projects","award":["2016YFB0501301"],"award-info":[{"award-number":["2016YFB0501301"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773383"],"award-info":[{"award-number":["61773383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.<\/jats:p>","DOI":"10.3390\/rs12091525","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T12:26:30Z","timestamp":1589199990000},"page":"1525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Improved Cloud Detection Method for GF-4 Imagery"],"prefix":"10.3390","volume":"12","author":[{"given":"Ming","family":"Lu","sequence":"first","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, Beijing 100094, China"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, Beijing 100094, China"}]},{"given":"Bangcheng","family":"Zhan","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, Beijing 100094, China"},{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475001, China"}]},{"given":"He","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Xue","family":"Yang","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, Beijing 100094, China"}]},{"given":"Xiaotian","family":"Lu","sequence":"additional","affiliation":[{"name":"Qian Xuesen Laboratory of Space Technology, Beijing 100094, China"}]},{"given":"Huachao","family":"Xiao","sequence":"additional","affiliation":[{"name":"Academy of Space information System, Xi\u2019an 710100, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, A., Zhong, B., Wu, S., and Liu, Q. (2017). Radiometric cross-calibration of GF-4 in multispectral bands. Remote Sens., 9.","DOI":"10.3390\/rs9030232"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/LGRS.2017.2768331","article-title":"Super-resolution for GaoFen-4 remote sensing images","volume":"15","author":"Li","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2991","DOI":"10.1080\/01431161.2018.1437296","article-title":"Ship target tracking based on a low-resolution optical satellite in geostationary orbit","volume":"39","author":"Liu","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, P., Sun, K., Li, D., Sui, H., and Zhang, Y. (2017). An emergency georeferencing framework for GF-4 imagery based on GCP prediction and dynamic RPC refinement. Remote Sens., 9.","DOI":"10.3390\/rs9101053"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1109\/LGRS.2017.2687700","article-title":"Application Potential of GF-4 Images for Dynamic Ship Monitoring","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Sun, K., Li, D., Bai, T., and Huang, C. (2017). Radiometric cross-calibration of gf-4 pms sensor based on assimilation of landsat-8 oli images. Remote Sens., 9.","DOI":"10.3390\/rs9080811"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yao, L., Liu, Y., and He, Y. (2018). A novel ship-tracking method for GF-4 satellite sequential images. Sensors, 18.","DOI":"10.3390\/s18072007"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wu, W., and Liu, W. (2018, January 18\u201320). Remote sensing recognition of residential areas based on GF-4 satellite image. Proceedings of the 2018 Fifth International Worksho on Earth Observation and Remote Sensing Applications (EORSA), Xi\u2019an, China.","DOI":"10.1109\/EORSA.2018.8598622"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.12.014","article-title":"Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data","volume":"126","author":"Lu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2016.12.008","article-title":"Multi-source remotely sensed data fusion for improving land cover classification","volume":"124","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4062","DOI":"10.1109\/TGRS.2018.2889677","article-title":"Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network","volume":"57","author":"Shao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/0034-4257(93)90046-Z","article-title":"Automatic cloud detection applied to NOAA-11\/AVHRR imagery","volume":"46","author":"Derrien","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qiu, S., Zhu, Z., and He, B. (2019). Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery. Remote Sens. Environ., 231.","DOI":"10.1016\/j.rse.2019.05.024"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Qiu, S., He, B., and Deng, C. (2018). Cloud and cloud shadow detection for Landsat images: The fundamental basis for analyzing Landsat time series. Remote Sensing Time Series Image Processing, CRC Press.","DOI":"10.1201\/9781315166636-1"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.05.024","article-title":"An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions","volume":"214","author":"Zhu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.14358\/PERS.72.10.1179","article-title":"Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) algorithm","volume":"72","author":"Irish","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.rse.2019.03.007","article-title":"Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks","volume":"225","author":"Chai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1109\/TGRS.2011.2164087","article-title":"Development of the Landsat Data Continuity Mission Cloud-Cover Assessment Algorithms","volume":"50","author":"Scaramuzza","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.3390\/rs6064907","article-title":"Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing","volume":"6","author":"Hughes","year":"2014","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1175\/1520-0426(2004)021<0159:CCOSRD>2.0.CO;2","article-title":"Cloud Classification of Satellite Radiance Data by Multicategory Support Vector Machines","volume":"21","author":"Lee","year":"2004","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neucom.2014.09.102","article-title":"A cloud image detection method based on SVM vector machine","volume":"169","author":"Li","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1080\/01431160902926632","article-title":"Improvement of cloud detection near sunrise and sunset by temporal-differencing and region-growing techniques with real-time SEVIRI","volume":"31","author":"Derrien","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2013.01.019","article-title":"Generation of new cloud masks from MODIS land surface reflectance products","volume":"133","author":"Liu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2013.02.019","article-title":"Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM\/ETM+ time series","volume":"134","author":"Goodwin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"59","DOI":"10.14358\/PERS.69.1.59","article-title":"Block adjustment of high-resolution satellite images described by rational polynomials","volume":"69","author":"Grodecki","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4105","DOI":"10.1109\/TGRS.2007.905312","article-title":"Cloud-Screening Algorithm for ENVISAT\/MERIS Multispectral Images","volume":"45","author":"Gomezchova","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0034-4257(02)00034-2","article-title":"An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images","volume":"82","author":"Zhang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"Mcfeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Silva, A.R., Silva, A.R., and Gouv\u00eaa, M.M. (2019). A novel model to simulate cloud dynamics with cellular automaton. Environ. Model. Softw., 122.","DOI":"10.1016\/j.envsoft.2019.104537"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.1109\/TVCG.2013.131","article-title":"An exploration framework to identify and track movement of cloud systems","volume":"19","author":"Doraiswamy","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5416","DOI":"10.1364\/AO.37.005416","article-title":"Fast phase-unwrapping algorithm based on a gray-scale mask and flood fill","volume":"37","author":"Asundi","year":"1998","journal-title":"Appl. Opt."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lu, M., Chen, B., Liao, X., Yue, T., Yue, H., Ren, S., Li, X., Nie, Z., and Xu, B. (2017). Forest Types Classification Based on Multi-Source Data Fusion. Remote Sens., 9.","DOI":"10.3390\/rs9111153"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1175\/1520-0477(1994)075<0757:IGITFO>2.0.CO;2","article-title":"Introducing GOES-I: The First of a New Generation of Geostationary Operational Environmental Satellites","volume":"75","author":"Menzel","year":"1994","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1109\/TGRS.2008.916208","article-title":"Spatial and Temporal Varying Thresholds for Cloud Detection in GOES Imagery","volume":"46","author":"Jedlovec","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cheung, W., and Hamarneh, G. (2007, January 12\u201315). N-sift: N-dimensional scale invariant feature transform for matching medical images. Proceedings of the 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA.","DOI":"10.1109\/ISBI.2007.356953"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-up robust features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G.R. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, ICCV, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1525\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:27:39Z","timestamp":1760174859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,11]]},"references-count":45,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12091525"],"URL":"https:\/\/doi.org\/10.3390\/rs12091525","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,5,11]]}}}