{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T05:35:40Z","timestamp":1773552940845,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Innovation Promotion Association, CAS","award":["cstc2020jcyj-msxmX0156"],"award-info":[{"award-number":["cstc2020jcyj-msxmX0156"]}]},{"name":"Youth Innovation Promotion Association, CAS","award":["KJQN201912905"],"award-info":[{"award-number":["KJQN201912905"]}]},{"name":"Defense Industrial Technology Development Program","award":["cstc2020jcyj-msxmX0156"],"award-info":[{"award-number":["cstc2020jcyj-msxmX0156"]}]},{"name":"Defense Industrial Technology Development Program","award":["KJQN201912905"],"award-info":[{"award-number":["KJQN201912905"]}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2020jcyj-msxmX0156"],"award-info":[{"award-number":["cstc2020jcyj-msxmX0156"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["KJQN201912905"],"award-info":[{"award-number":["KJQN201912905"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["cstc2020jcyj-msxmX0156"],"award-info":[{"award-number":["cstc2020jcyj-msxmX0156"]}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJQN201912905"],"award-info":[{"award-number":["KJQN201912905"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In observations of Earth, the existence of clouds affects the quality and usability of optical remote sensing images in practical applications. Many cloud removal methods have been proposed to solve this issue. Among these methods, synthetic aperture radar (SAR)-based methods have more potential than others because SAR imaging is hardly affected by clouds, and can reflect ground information differences and changes. While SAR images used as auxiliary information for cloud removal may be blurred and noisy, the similar non-local information of spectral and electromagnetic features cannot be effectively utilized by traditional cloud removal methods. To overcome these weaknesses, we propose a novel cloud removal method using SAR-optical data fusion and a graph-based feature aggregation network (G-FAN). First, cloudy optical images and contemporary SAR images are concatenated and transformed into hyper-feature maps by pre-convolution. Second, the hyper-feature maps are inputted into the G-FAN to reconstruct the missing data of the cloud-covered area by aggregating the electromagnetic backscattering information of the SAR image, and the spectral information of neighborhood and non-neighborhood pixels in the optical image. Finally, post-convolution and a long skip connection are adopted to reconstruct the final predicted cloud-free images. Both the qualitative and quantitative experimental results from the simulated data and real data experiments show that our proposed method outperforms traditional deep learning methods for cloud removal.<\/jats:p>","DOI":"10.3390\/rs14143374","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:12:40Z","timestamp":1657757560000},"page":"3374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Cloud Removal with SAR-Optical Data Fusion and Graph-Based Feature Aggregation Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5793-1143","authenticated-orcid":false,"given":"Shanjing","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Army Logistics University, Chongqing 401311, China"}]},{"given":"Wenjuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yuxi","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0319-7753","authenticated-orcid":false,"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1109\/JSTARS.2022.3148139","article-title":"Progress and challenges in intelligent remote sensing satellite systems","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3937","DOI":"10.1109\/JSTARS.2021.3062411","article-title":"Research on generic optical remote sensing products: A review of scientific exploration, technology research, and engineering application","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111949","DOI":"10.1016\/j.rse.2020.111949","article-title":"Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS","volume":"247","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, X., Hong, D., Gao, L., Zhang, B., and Chanussot, J. (2021). Transferable deep learning from time series of Landsat data for national land-cover mapping with noisy labels: A case study of China. Remote Sens., 13.","DOI":"10.3390\/rs13214194"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Youssefi, F., Zoej, M.J.V., Hanafi-Bojd, A.A., Dariane, A.B., Khaki, M., Safdarinezhad, A., and Ghaderpour, E. (2022). Temporal monitoring and predicting of the abundance of malaria vectors using time series analysis of remote sensing data through Google Earth Engine. Sensors, 22.","DOI":"10.3390\/s22051942"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Duan, C., Pan, J., and Li, R. (2020). Thick cloud removal of remote sensing images using temporal smoothness and sparsity regularized tensor optimization. Remote Sens., 12.","DOI":"10.3390\/rs12203446"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5605914","DOI":"10.1109\/TGRS.2021.3095067","article-title":"Reconstructing missing information of remote sensing data contaminated by large and thick clouds based on an improved multitemporal dictionary learning method","volume":"60","author":"Xia","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free landsat etm plus data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, C., Zhang, Y., Chen, P., Lai, C., Chen, Y., Cheng, J., and Ko, M. (2019). Clouds classification from Sentinel-2 imagery with deep residual learning and semantic image segmentation. Remote Sens., 11.","DOI":"10.3390\/rs11020119"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2019.03.039","article-title":"A cloud detection algorithm for satellite imagery based on deep learning","volume":"229","author":"Jeppesen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1080\/01431161.2019.1667548","article-title":"Satellite data cloud detection using deep learning supported by hyperspectral data","volume":"41","author":"Sun","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5410012","DOI":"10.1109\/TGRS.2022.3175613","article-title":"Dual-branch network for cloud and cloud shadow segmentation","volume":"60","author":"Lu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111952","DOI":"10.1016\/j.rse.2020.111952","article-title":"Deeply synergistic optical and SAR time series for crop dynamic monitoring","volume":"247","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108021","DOI":"10.1016\/j.geomorph.2021.108021","article-title":"Exploring event landslide mapping using Sentinel-1 SAR backscatter products","volume":"397","author":"Santangelo","year":"2022","journal-title":"Geomorphology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/JSTARS.2020.3043628","article-title":"Combined Sentinel-1A with Sentinel-2A to estimate soil moisture in farmland","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","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_17","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":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"5963","DOI":"10.1109\/TGRS.2019.2903594","article-title":"A coarse-to-fine framework for cloud removal in remote sensing image sequence","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"112001","DOI":"10.1016\/j.rse.2020.112001","article-title":"Thick cloud removal in Landsat images based on autoregression of Landsat time-series data","volume":"249","author":"Cao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"112902","DOI":"10.1016\/j.rse.2022.112902","article-title":"Attention mechanism-based generative adversarial networks for cloud removal in Landsat images","volume":"271","author":"Xu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_25","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_26","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_27","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_28","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\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519215"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bermudez, J., Happ, P., Oliveira, D., and Feitosa, R. (2018, January 10\u201312). Sar to optical image synthesis for cloud removal with generative adversarial networks. Proceedings of the ISPRS Mid-Term Symposium Innovative Sensing\u2014From Sensors to Methods and Applications, Karlsruhe, Germany.","DOI":"10.5194\/isprs-annals-IV-1-5-2018"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gao, J., Yi, Y., Wei, T., and Zhang, G. (2021). Sentinel-2 cloud removal considering ground changes by fusing multitemporal SAR and optical images. Remote Sens., 13.","DOI":"10.3390\/rs13193998"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, W., and Yokoya, N. (2018). Multi-temporal sentinel-1 and-2 data fusion for optical image simulation. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100389"},{"key":"ref_32","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_33","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2018, January 21\u201325). Graph attention networks. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.procs.2021.07.047","article-title":"A brief review of graph convolutional neural network based learning for classifying remote sensing images","volume":"191","author":"Baroud","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.1109\/TGRS.2018.2890705","article-title":"CoSpace: Common subspace learning from hyperspectral-multispectral correspondences","volume":"57","author":"Hong","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.isprsjprs.2018.10.006","article-title":"Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification","volume":"147","author":"Hong","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph convolutional networks for hyperspectral image classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","unstructured":"Kipf, T., and Welling, M. (2017, January 24\u201326). Semi-supervised classification with graph convolutional networks. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105477","DOI":"10.1109\/ACCESS.2021.3100328","article-title":"Joint image dehazing and super-resolution: Closed shared source residual attention fusion network","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8226","DOI":"10.1109\/TIP.2020.3013166","article-title":"Deep graph-convolutional image denoising","volume":"29","author":"Valsesia","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Valsesia, D., Fracastoro, G., and Magli, E. (2019, January 22\u201325). Image denoising with graph-convolutional neural networks. Proceedings of the IEEE International Conference on Image Processing (ICIP), Taipei, China.","DOI":"10.1109\/ICIP.2019.8803367"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"39254","DOI":"10.1109\/ACCESS.2020.2967028","article-title":"Split-attention multiframe alignment network for image restoration","volume":"8","author":"Yu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss functions for image restoration with neural networks","volume":"3","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L., Qiu, C., and Zhu, X. (2019, January 18\u201320). Aggregating cloud-free Sentinel-2 images with Google Earth Engine. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Munich, Germany.","DOI":"10.5194\/isprs-annals-IV-2-W7-145-2019"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.rse.2018.04.046","article-title":"Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects","volume":"215","author":"Frantz","year":"2018","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:49:43Z","timestamp":1760140183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,13]]},"references-count":48,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143374"],"URL":"https:\/\/doi.org\/10.3390\/rs14143374","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,13]]}}}