{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:57:33Z","timestamp":1784134653851,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T00:00:00Z","timestamp":1682121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071422"],"award-info":[{"award-number":["42071422"]}],"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":["2018YFC0706004"],"award-info":[{"award-number":["2018YFC0706004"]}],"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":["Yz2022018"],"award-info":[{"award-number":["Yz2022018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["42071422"],"award-info":[{"award-number":["42071422"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0706004"],"award-info":[{"award-number":["2018YFC0706004"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["Yz2022018"],"award-info":[{"award-number":["Yz2022018"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"College Students\u2019 Innovative Entrepreneurial Training Plan Program","award":["42071422"],"award-info":[{"award-number":["42071422"]}]},{"name":"College Students\u2019 Innovative Entrepreneurial Training Plan Program","award":["2018YFC0706004"],"award-info":[{"award-number":["2018YFC0706004"]}]},{"name":"College Students\u2019 Innovative Entrepreneurial Training Plan Program","award":["Yz2022018"],"award-info":[{"award-number":["Yz2022018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cloud contamination is a common issue that severely reduces the quality of optical satellite images in remote sensing fields. With the rapid development of deep learning technology, cloud contamination is expected to be addressed. In this paper, we propose Denoising Diffusion Probabilistic Model-Cloud Removal (DDPM-CR), a novel cloud removal network that can effectively remove both thin and thick clouds in optical image scenes. Our network leverages the denoising diffusion probabilistic model (DDPM) architecture to integrate both clouded optical and auxiliary SAR images as input to extract DDPM features, providing significant information for missing information retrieval. Additionally, we propose a cloud removal head adopting the DDPM features with an attention mechanism at multiple scales to remove clouds. To achieve better network performance, we propose a cloud-oriented loss that considers both high- and low-frequency image information as well as cloud regions in the training procedure. Our ablation and comparative experiments demonstrate that the DDPM-CR network outperforms other methods under various cloud conditions, achieving better visual effects and accuracy metrics (MAE = 0.0229, RMSE = 0.0268, PSNR = 31.7712, and SSIM = 0.9033). These results suggest that the DDPM-CR network is a promising solution for retrieving missing information in either thin or thick cloud-covered regions, especially when using auxiliary information such as SAR data.<\/jats:p>","DOI":"10.3390\/rs15092217","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T02:06:11Z","timestamp":1682301971000},"page":"2217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Denoising Diffusion Probabilistic Feature-Based Network for Cloud Removal in Sentinel-2 Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Ran","family":"Jing","sequence":"first","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuzhou","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengxian","family":"Lu","sequence":"additional","affiliation":[{"name":"Henan Engineering Research Center of Environmental Laser Remote Sensing Technology and Application, Nanyang 473061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Henan Engineering Research Center of Environmental Laser Remote Sensing Technology and Application, Nanyang 473061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenji","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112419","DOI":"10.1016\/j.rse.2021.112419","article-title":"Recurrent-based regression of Sentinel time series for continuous vegetation monitoring","volume":"263","author":"Garioud","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_2","first-page":"102514","article-title":"Integrating remote sensing and geospatial big data for urban land use mapping: A review","volume":"103","author":"Yin","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113655","DOI":"10.1016\/j.jenvman.2021.113655","article-title":"Detecting ecological spatial-temporal changes by remote sensing ecological index with local adaptability","volume":"299","author":"Zhu","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.1109\/TGRS.2012.2227333","article-title":"Spatial and temporal distribution of clouds observed by MODIS onboard the Terra and Aqua satellites","volume":"51","author":"King","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.rse.2016.09.019","article-title":"Producing cloud-free MODIS snow cover products with conditional probability interpolation and meteorological data","volume":"186","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","first-page":"214","article-title":"Anisotropic inpainting of the hypercube","volume":"9","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1109\/TGRS.2008.2005780","article-title":"A MAP-based algorithm for destriping and inpainting of remotely sensed images","volume":"47","author":"Shen","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2019.09.003","article-title":"Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition","volume":"157","author":"Chen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"2409","DOI":"10.1109\/TGRS.2011.2173499","article-title":"Quantitative restoration for MODIS band 6 on Aqua","volume":"50","author":"Gladkova","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/TGRS.2013.2245509","article-title":"Compressed sensing-based inpainting of aqua moderate resolution imaging spectroradiometer band 6 using adaptive spectrum-weighted sparse Bayesian dictionary learning","volume":"52","author":"Shen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1109\/TGRS.2015.2486780","article-title":"Thin cloud removal based on signal transmission principles and spectral mixture analysis","volume":"54","author":"Xu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1109\/LGRS.2018.2874084","article-title":"Haze and thin cloud removal via sphere model improved dark channel prior","volume":"16","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Li, Z., Shen, H., Cheng, Q., Li, W., and Zhang, L. (2019). Thick cloud removal in high-resolution satellite images using stepwise radiometric adjustment and residual correction. Remote Sens., 11.","DOI":"10.3390\/rs11161925"},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/TGRS.2017.2777886","article-title":"Reconstructing cloud-contaminated multispectral images with contextualized autoencoder neural networks","volume":"56","author":"Malek","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"6371","DOI":"10.1109\/TGRS.2020.3027819","article-title":"Single image cloud removal using U-Net and generative adversarial networks","volume":"59","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2022.08.002","article-title":"GLF-CR: SAR-enhanced cloud removal with global\u2013local fusion","volume":"192","author":"Xu","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.isprsjprs.2020.05.013","article-title":"Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion","volume":"166","author":"Meraner","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1109\/TGRS.2020.2994349","article-title":"Simultaneous cloud detection and removal from bitemporal remote sensing images using cascade convolutional neural networks","volume":"59","author":"Ji","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jing, R., Duan, F., Lu, F., Zhang, M., and Zhao, W. (2022). An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions. Remote Sens., 14.","DOI":"10.3390\/rs14071632"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Grohnfeldt, C., Schmitt, M., and Zhu, X. (2018, January 22\u201327). A conditional generative adversarial network to fuse SAR and multispectral optical data for cloud removal from Sentinel-2 images. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519215"},{"key":"ref_28","first-page":"1","article-title":"Cloud Removal Using Multimodal GAN With Adversarial Consistency Loss","volume":"19","author":"Zhao","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","first-page":"1","article-title":"Memory-Oriented Unpaired Learning for Single Remote Sensing Image Dehazing","volume":"19","author":"Chen","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"034520","DOI":"10.1117\/1.JRS.16.034520","article-title":"Cloud removal for optical remote sensing imagery using the SPA-CycleGAN network","volume":"16","author":"Jing","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3131035","article-title":"Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation","volume":"60","author":"Darbaghshahi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.1080\/01431161.2022.2072179","article-title":"Multi-Scale translation method from SAR to optical remote sensing images based on conditional generative adversarial network","volume":"43","author":"Kong","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fuentes Reyes, M., Auer, S., Merkle, N., Henry, C., and Schmitt, M. (2019). Sar-to-optical image translation based on conditional generative adversarial networks\u2014Optimization, opportunities and limits. Remote Sens., 11.","DOI":"10.3390\/rs11172067"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., and Norouzi, M. (2022). Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A.G., and Milanfar, P. (2022, January 19\u201324). Deblurring via stochastic refinement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01581"},{"key":"ref_36","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., and Poole, B. (2020). Score-based generative modeling through stochastic differential equations. arXiv."},{"key":"ref_37","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_39","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (2017). Searching for activation functions. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.isprsjprs.2018.07.006","article-title":"Cloud\/shadow detection based on spectral indices for multi\/hyperspectral optical remote sensing imagery","volume":"144","author":"Zhai","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016, January 8\u201316). Perceptual losses for real-time style transfer and super-resolution. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5866","DOI":"10.1109\/TGRS.2020.3024744","article-title":"Multisensor data fusion for cloud removal in global and all-season sentinel-2 imagery","volume":"59","author":"Ebel","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"120758","DOI":"10.1016\/j.jclepro.2020.120758","article-title":"Sustainable development and environmental restoration in Lake Erhai, China","volume":"258","author":"Lin","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.sigpro.2017.01.036","article-title":"Haze removal for a single visible remote sensing image","volume":"137","author":"Liu","year":"2017","journal-title":"Signal Process."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, HI, USA.","DOI":"10.1109\/CVPR.2017.632"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2217\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:21:06Z","timestamp":1760124066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,22]]},"references-count":48,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092217"],"URL":"https:\/\/doi.org\/10.3390\/rs15092217","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,22]]}}}