{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T23:55:41Z","timestamp":1770422141019,"version":"3.49.0"},"reference-count":80,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Introduction plan of high-end foreign experts","award":["G2021025006L"],"award-info":[{"award-number":["G2021025006L"]}]},{"name":"the Introduction plan of high-end foreign experts","award":["42271412"],"award-info":[{"award-number":["42271412"]}]},{"name":"the National Natural Science Foundation of China","award":["G2021025006L"],"award-info":[{"award-number":["G2021025006L"]}]},{"name":"the National Natural Science Foundation of China","award":["42271412"],"award-info":[{"award-number":["42271412"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A stable and reliable cloud detection algorithm is an important step of optical satellite data preprocessing. Existing threshold methods are mostly based on classifying spectral features of isolated individual pixels and do not contain or incorporate the spatial information. This often leads to misclassifications of bright surfaces, such as human-made structures or snow\/ice. Multi-temporal methods can alleviate this problem, but cloud-free images of the scene are difficult to obtain. To deal with this issue, we extended four deep-learning Convolutional Neural Network (CNN) models to improve the global cloud detection accuracy for Landsat imagery. The inputs are simplified as all discrete spectral channels from visible to short wave infrared wavelengths through radiometric calibration, and the United States Geological Survey (USGS) global Landsat 8 Biome cloud-cover assessment dataset is randomly divided for model training and validation independently. Experiments demonstrate that the cloud mask of the extended U-net model (i.e., UNmask) yields the best performance among all the models in estimating the cloud amounts (cloud amount difference, CAD = \u22120.35%) and capturing the cloud distributions (overall accuracy = 94.9%) for Landsat 8 imagery compared with the real validation masks; in particular, it runs fast and only takes about 41 \u00b1 5.5 s for each scene. Our model can also actually detect broken and thin clouds over both dark and bright surfaces (e.g., urban and barren). Last, the UNmask model trained for Landsat 8 imagery is successfully applied in cloud detections for the Sentinel-2 imagery (overall accuracy = 90.1%) via transfer learning. These prove the great potential of our model in future applications such as remote sensing satellite data preprocessing.<\/jats:p>","DOI":"10.3390\/rs15061706","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T06:00:01Z","timestamp":1679464801000},"page":"1706","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Convolutional Neural Network-Driven Improvements in Global Cloud Detection for Landsat 8 and Transfer Learning on Sentinel-2 Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6696-453X","authenticated-orcid":false,"given":"Shulin","family":"Pang","sequence":"first","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Lin","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Yanan","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Yutiao","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8803-7056","authenticated-orcid":false,"given":"Jing","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1175\/1520-0469(1989)046<1922:ERBACS>2.0.CO;2","article-title":"Earth Radiation Budget and Cloudiness Simulations with a General Circulation Model","volume":"46","author":"Harshvardhan","year":"1989","journal-title":"J. Atmos. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1126\/science.243.4887.57","article-title":"Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment","volume":"243","author":"Ramanathan","year":"1989","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7172","DOI":"10.1002\/2015JD024722","article-title":"A Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a prior surface reflectance database","volume":"121","author":"Sun","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13338","DOI":"10.1002\/2017JD026922","article-title":"A simple and universal aerosol retrieval algorithm for Landsat series images over complex surfaces","volume":"122","author":"Wei","year":"2017","journal-title":"J. Geophys. Res. -Atmos."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.5194\/acp-23-1511-2023","article-title":"Ground-level gaseous pollutants (NO2, SO2, and CO) in China: Daily seamless mapping and spatiotemporal variations","volume":"23","author":"Wei","year":"2023","journal-title":"Atmos. Chem. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1080\/01431160010006926","article-title":"Cloud cover in Landsat observations of the Brazilian Amazon","volume":"22","author":"Asner","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"D19105","DOI":"10.1029\/2003JD004457","article-title":"Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data","volume":"109","author":"Zhang","year":"2004","journal-title":"J. Geophys. Res. -Atmos."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0273-1177(85)90319-9","article-title":"ISCCP cloud analysis algorithm intercomparison","volume":"5","author":"Rossow","year":"1985","journal-title":"Adv. Space Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1175\/1520-0477(1991)072<0002:ICDP>2.0.CO;2","article-title":"ISCCP Cloud Data Products","volume":"72","author":"Rossow","year":"1991","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1175\/1520-0442(1993)006<2341:CDUSMO>2.0.CO;2","article-title":"Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for ISCCP","volume":"6","author":"Rossow","year":"1993","journal-title":"J. Clim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0273-1177(91)90402-6","article-title":"Global distribution of cloud cover derived from NOAA\/AVHRR operational satellite data","volume":"11","author":"Stowe","year":"1991","journal-title":"Adv. Space Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/01431168808954841","article-title":"An improved method for detecting clear sky and cloudy radiances from AVHRR data","volume":"9","author":"Saunders","year":"1988","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","first-page":"165","article-title":"Optical Properties of Clouds Derived from Fully Cloudy AVHRR Pixels","volume":"62","author":"Kriebel","year":"1989","journal-title":"Bcitr. Phys. Atmosph."},{"key":"ref_15","first-page":"348","article-title":"Landsat 7 automatic cloud cover assessment","volume":"4049","author":"Irish","year":"2000","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TGRS.2003.811817","article-title":"Quantitative assessment of a haze suppression methodology for satellite imagery: Effect on land cover classification performance","volume":"41","author":"Zhang","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","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_19","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":"Guanter","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.isprsjprs.2008.12.007","article-title":"Use of Markov Random Fields for automatic cloud\/shadow detection on high resolution optical images","volume":"64","author":"Andre","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","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_22","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_23","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."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01431161.2018.1506183","article-title":"A new Landsat 8 cloud discrimination algorithm using thresholding tests","volume":"39","author":"Oishi","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2018.09.029","article-title":"New neural network cloud mask algorithm based on radiative transfer simulations","volume":"219","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"32141","DOI":"10.1029\/1998JD200032","article-title":"Discriminating clear sky from clouds with MODIS","volume":"103","author":"Ackerman","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.rse.2007.05.016","article-title":"Evaluation of MODIS snow cover and cloud mask and its application in Northern Xinjiang, China","volume":"112","author":"Wang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112136","DOI":"10.1016\/j.rse.2020.112136","article-title":"Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: Spatiotemporal variations and policy implications","volume":"252","author":"Wei","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.1080\/01431161.2019.1580788","article-title":"Energy-based cloud detection in multispectral images based on the SVM technique","volume":"40","author":"Sui","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.asr.2018.04.030","article-title":"Introducing two Random Forest based methods for cloud detection in remote sensing images","volume":"62","author":"Ghasemian","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112005","DOI":"10.1016\/j.rse.2020.112005","article-title":"Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches","volume":"248","author":"Wei","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"123649","DOI":"10.1109\/ACCESS.2020.3005687","article-title":"Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21780","DOI":"10.1109\/JSEN.2022.3197235","article-title":"Pseudo RGB-D Face Recognition","volume":"22","author":"Jin","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/978-3-030-87589-3_45","article-title":"VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding Based Deep Learning","volume":"12966","author":"Zhao","year":"2021","journal-title":"Mach. Learn. Med. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/978-3-030-88210-5_16","article-title":"Compound Figure Separation of Biomedical Images with Side Loss","volume":"13003","author":"Yao","year":"2021","journal-title":"Deep. Gener. Model. Data Augment. Label. Imperfections"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","unstructured":"Goff, M.L., Tourneret, J.-Y., Wendt, H., Ortner, M., and Spigai, M. (2017). Deep Learning for Cloud Detection, International Conference of Pattern Recognition Systems (ICPRS)."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zi, Y., Xie, F., and Jiang, Z. (2018). A Cloud Detection Method for Landsat 8 Images Based on PCANet. Remote Sens., 10.","DOI":"10.3390\/rs10060877"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ozkan, S., Efendioglu, M., and Demirpolat, C. (2018, January 22\u201327). Cloud detection from RGB color remote sensing images with deep pyramid networks. Proceedings of the 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519570"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"111203","DOI":"10.1016\/j.rse.2019.05.022","article-title":"Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network","volume":"230","author":"Wieland","year":"2019","journal-title":"Remote Sens. Environ."},{"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."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"15844","DOI":"10.1109\/ACCESS.2018.2810849","article-title":"Improvement of Generalization Ability of Deep CNN via Implicit Regularization in Two-Stage Training Process","volume":"6","author":"Zheng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_50","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Shao, X., and Zhang, W. (2021, January 10\u201317). SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00648"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/LGRS.2017.2752750","article-title":"MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification","volume":"14","author":"Lin","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1109\/LGRS.2019.2894734","article-title":"Synthesis of Multispectral Optical Images From SAR\/Optical Multitemporal Data Using Conditional Generative Adversarial Networks","volume":"16","author":"Castro","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.isprsjprs.2020.06.021","article-title":"Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion","volume":"166","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Nyborg, J., and Assent, I. (2021, January 15\u201318). Weakly-Supervised Cloud Detection with Fixed-Point GANs. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA.","DOI":"10.1109\/BigData52589.2021.9671405"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/LGRS.2019.2955071","article-title":"Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images","volume":"17","author":"Wu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","unstructured":"Arjovsky, M., and Bottou, L. (2017). Towards Principled Methods for Training Generative Adversarial Networks. Stat, 1050."},{"key":"ref_58","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 4). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, J., Du, B., Xia, G.S., and Tao, D. (2022). An Empirical Study of Remote Sensing Pretraining. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2022.3176603"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, Q., Xu, Y., Zhang, J., Du, B., Tao, D., and Zhang, L. (2022). Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2022.3222818"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.11.024","article-title":"Transferring deep learning models for cloud detection between Landsat-8 and Proba-V","volume":"160","author":"Laparra","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"113197","DOI":"10.1016\/j.rse.2022.113197","article-title":"A hybrid generative adversarial network for weakly-supervised cloud detection in multispectral images","volume":"280","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_64","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). Medical Image Computing and Computer-Assisted Intervention (MICCAI), Medical Image Computing and Computer-Assisted Intervention (MICCAI)."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chen, L.C.E., Zhu, Y.K., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_67","first-page":"170","article-title":"SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity","volume":"50","author":"Quintano","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_68","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_69","unstructured":"Francis, A., Mrziglod, J., Sidiropoulos, P., and Muller, J.-P. (2020). Sentinel-2 Cloud Mask Catalogue. Zenodo."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. Acm"},{"key":"ref_71","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K.P., and Yuille, A.L. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20\u201325). RepVGG: Making VGG-style ConvNets Great Again. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/BF02289261","article-title":"Note on the \u201ccorrection for continuity\u201d in testing the significance of the difference between correlated proportions","volume":"13","author":"Edwards","year":"1948","journal-title":"Psychometrika"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2003.08.010","article-title":"Intercalibration of vegetation indices from different sensor systems","volume":"88","author":"Steven","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Odena, A., Dumoulin, V., and Olah, C. (2016). Deconvolution and Checkerboard Artifacts. Distill.","DOI":"10.23915\/distill.00003"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018, January 12\u201315). Understanding Convolution for Semantic Segmentation. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_80","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\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1706\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:00:30Z","timestamp":1760122830000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,22]]},"references-count":80,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061706"],"URL":"https:\/\/doi.org\/10.3390\/rs15061706","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,22]]}}}