{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T18:57:48Z","timestamp":1769281068359,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,17]],"date-time":"2019-08-17T00:00:00Z","timestamp":1566000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFA0605500"],"award-info":[{"award-number":["2018YFA0605500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671334"],"award-info":[{"award-number":["61671334"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601357"],"award-info":[{"award-number":["41601357"]}],"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>Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) is proposed, which is aimed at effectively removing the clouds in high-resolution images for the generation of high-quality and spatially contiguous urban geographical maps. The basic idea of SRARC is that the complementary information in adjacent temporal satellite images can be utilized for the seamless recovery of cloud-contaminated areas in the target image after precise radiometric adjustment. To this end, the SRARC method first optimizes the given cloud mask of the target image based on superpixel segmentation, which is conducted to ensure that the labeled cloud boundaries go through homogeneous areas of the target image, to ensure a seamless reconstruction. Stepwise radiometric adjustment is then used to adjust the radiometric information of the complementary areas in the auxiliary image, step by step, and clouds in the target image can be removed by the replacement with the adjusted complementary areas. Finally, residual correction based on global optimization is used to further reduce the radiometric differences between the recovered areas and the cloud-free areas. The final cloud removal results are then generated. High-resolution images with different spatial resolutions and land-cover change patterns were used in both simulated and real-data cloud removal experiments. The results suggest that SRARC can achieve a better performance than the other compared methods, due to the superiority of the radiometric adjustment and spatial detail preservation. SRARC is thus a promising approach that has the potential for routine use, to support applications based on high-resolution satellite images.<\/jats:p>","DOI":"10.3390\/rs11161925","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"1925","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Thick Cloud Removal in High-Resolution Satellite Images Using Stepwise Radiometric Adjustment and Residual Correction"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5635-8499","authenticated-orcid":false,"given":"Zhiwei","family":"Li","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Huanfeng","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0571-4083","authenticated-orcid":false,"given":"Qing","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Urban Design, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6318-401X","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Liangpei","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.isprsjprs.2013.05.008","article-title":"Classifying a high resolution image of an urban area using super-object information","volume":"83","author":"Johnson","year":"2013","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I. (2017). Operational high resolution land cover map production at the country scale using satellite image time series. Remote Sens., 9.","DOI":"10.3390\/rs9010095"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.rse.2017.01.026","article-title":"Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery","volume":"191","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MGRS.2015.2441912","article-title":"Missing information reconstruction of remote sensing data: A technical review","volume":"3","author":"Shen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1080\/01431160701250416","article-title":"Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach","volume":"28","author":"Zhang","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TIP.2010.2042098","article-title":"Image Inpainting by patch propagation using patch sparsity","volume":"19","author":"Xu","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TGRS.2012.2237521","article-title":"Inpainting for remotely sensed images with a multichannel nonlocal total variation model","volume":"52","author":"Cheng","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TIP.2004.833105","article-title":"Region filling and object removal by exemplar-pased image inpainting","volume":"13","author":"Criminisi","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1080\/01431160802549294","article-title":"Restoration of clouded pixels in multispectral remotely sensed imagery with cokriging","volume":"30","author":"Zhang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/TGRS.2008.2010454","article-title":"A Bandelet-based inpainting technique for clouds removal from remotely sensed images","volume":"47","author":"Maalouf","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3998","DOI":"10.1109\/TGRS.2012.2227329","article-title":"Missing-area reconstruction in multispectral images under a compressive sensing perspective","volume":"51","author":"Lorenzi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Meng, F., Yang, X., Zhou, C., and Li, Z. (2017). A sparse dictionary learning-based adaptive patch inpainting method for thick clouds removal from high-spatial resolution remote sensing imagery. Sensors, 17.","DOI":"10.3390\/s17092130"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1109\/LSP.2017.2703092","article-title":"Missing information reconstruction for single remote sensing images using structure-preserving global optimization","volume":"24","author":"Cheng","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.rse.2009.11.001","article-title":"Comparison of cloud-reconstruction methods for time series of composite NDVI data","volume":"114","author":"Julien","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2018.04.005","article-title":"A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud","volume":"141","author":"Zeng","year":"2018","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4289","DOI":"10.1109\/TGRS.2018.2810271","article-title":"On the generation of gapless and seamless daily surface reflectance data","volume":"56","author":"Yang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Angel, Y., Houborg, R., and McCabe, M.F. (2019). Reconstructing cloud contaminated pixels using spatiotemporal covariance functions and multitemporal hyperspectral imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101145"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.isprsjprs.2014.02.015","article-title":"Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model","volume":"92","author":"Cheng","year":"2014","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/TGRS.2012.2237408","article-title":"Patch-based information reconstruction of cloud-contaminated multitemporal images","volume":"52","author":"Lin","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3656","DOI":"10.1109\/TGRS.2017.2656162","article-title":"Multitemporal Landsat missing data recovery based on tempo-spectral angle model","volume":"55","author":"Gao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"025005","DOI":"10.1117\/1.JRS.11.025005","article-title":"Automatic cloud-free image generation from high-resolution multitemporal imagery","volume":"11","author":"Han","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TGRS.2005.861929","article-title":"Contextual reconstruction of cloud-contaminated multitemporal multispectral images","volume":"44","author":"Melgani","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/LGRS.2011.2173290","article-title":"A modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat images","volume":"9","author":"Zhu","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2012.12.012","article-title":"Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method","volume":"131","author":"Zeng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TGRS.2016.2580576","article-title":"Spatially and temporally weighted regression: a novel method to produce continuous cloud-free Landsat imagery","volume":"55","author":"Chen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Du, W., Qin, Z., Fan, J., Gao, M., Wang, F., and Abbasi, B. (2019). An efficient approach to remove thick cloud in VNIR bands of multi-temporal remote sensing images. Remote Sens., 11.","DOI":"10.3390\/rs11111284"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7086","DOI":"10.1109\/TGRS.2014.2307354","article-title":"Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2998","DOI":"10.1109\/TGRS.2015.2509860","article-title":"Cloud removal based on sparse representation via multitemporal dictionary learning","volume":"54","author":"Xu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/JSTARS.2017.2655101","article-title":"Removal of Optically Thick clouds from high-resolution satellite imagery using dictionary group learning and interdictionary nonlocal joint sparse coding","volume":"10","author":"Li","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.1109\/TGRS.2018.2810208","article-title":"Missing data reconstruction in remote sensing image with a unified spatial\u2013temporal\u2013spectral deep convolutional neural network","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tahsin, S., Medeiros, S.C., Hooshyar, M., and Singh, A. (2017). optical cloud pixel recovery via machine learning. Remote Sens., 9.","DOI":"10.3390\/rs9060527"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2018.12.013","article-title":"Cloud removal in remote sensing images using nonnegative matrix factorization and error correction","volume":"148","author":"Li","year":"2019","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/JSTARS.2019.2898348","article-title":"A spatiotemporal fusion based cloud removal method for remote sensing images with land cover changes","volume":"12","author":"Shen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","first-page":"410","article-title":"Cloud removal of optical image using SAR data for ALOS applications. experimenting on simulated ALOS data","volume":"29","author":"Hoan","year":"2009","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.3390\/rs5062973","article-title":"Removal of optically thick clouds from multi-spectral satellite images using multi-frequency SAR Data","volume":"5","author":"Eckardt","year":"2013","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1109\/LGRS.2014.2377476","article-title":"Cloud removal from optical satellite imagery with SAR imagery using sparse representation","volume":"12","author":"Huang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1080\/01431161.2012.720045","article-title":"Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA","volume":"34","author":"Jin","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.5194\/hess-13-1361-2009","article-title":"hydrology and earth system sciences cloud removal methodology from MODIS snow cover product","volume":"13","author":"Gafurov","year":"2009","journal-title":"Hydrol. Earth Syst. Sci"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.rse.2011.01.006","article-title":"Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology","volume":"115","author":"Paudel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TIT.1962.1057692","article-title":"Visual pattern recognition by moment invariants","volume":"8","author":"Hu","year":"1962","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1145\/882262.882269","article-title":"Poisson image editing","volume":"22","author":"Gangnet","year":"2003","journal-title":"Acm Trans. Graph."},{"key":"ref_45","unstructured":"Storey, J., Scaramuzza, P., Schmidt, G., and Barsi, J. (2005, January 23\u201327). Landsat 7 scan line corrector-off gap-filled product development. Proceedings of the Pecora 16 Conference on Global Priorities in Land Remote Sensing, Sioux Falls, SD, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5226","DOI":"10.1109\/TIP.2013.2283400","article-title":"Single-image noise level estimation for blind denoising","volume":"22","author":"Liu","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2019.02.017","article-title":"Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors","volume":"150","author":"Li","year":"2019","journal-title":"Isprs J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1925\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:52Z","timestamp":1760188312000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1925"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,17]]},"references-count":47,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11161925"],"URL":"https:\/\/doi.org\/10.3390\/rs11161925","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,17]]}}}