{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:23:20Z","timestamp":1772299400412,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T00:00:00Z","timestamp":1595376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, a number of different procedures have been proposed for segmentation of remote sensing images, basing on spectral information. Model-based and machine learning strategies have been investigated in several studies. This work presents a comprehensive overview and an unbiased comparison of the most adopted segmentation strategies: Support Vector Machines (SVM), Random Forests, Neural networks, Sen2Cor, FMask and MAJA. We used a training set for learning and two different independent sets for testing. The comparison accounted for 135 images acquired from 54 different worldwide sites. We observed that machine learning segmentations are extremely reliable when the training and test are homogeneous. SVM performed slightly better than other methods. In particular, when using heterogeneous test data, SVM remained the most accurate segmentation method while state-of-the-art model-based methods such as MAJA and FMask obtained better sensitivity and precision, respectively. Therefore, even if each method has its specific advantages and drawbacks, SVM resulted in a competitive option for remote sensing applications.<\/jats:p>","DOI":"10.3390\/rs12152355","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T11:26:01Z","timestamp":1595503561000},"page":"2355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Machine Learning for Cloud Detection of Globally Distributed Sentinel-2 Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Roberto","family":"Cilli","sequence":"first","affiliation":[{"name":"Dipartimento Interateneo di Fisica \u201cM. Merlin\u201d, Universit\u00e0 degli Studi di Bari Aldo Moro, 70121 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5968-8642","authenticated-orcid":false,"given":"Alfonso","family":"Monaco","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70121 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0211-0783","authenticated-orcid":false,"given":"Nicola","family":"Amoroso","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70121 Bari, Italy"},{"name":"Dipartimento di Farmacia-Scienze del Farmaco, Universit\u00e0 degli Studi di Bari Aldo Moro, 70121 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Tateo","sequence":"additional","affiliation":[{"name":"Dipartimento Interateneo di Fisica \u201cM. Merlin\u201d, Universit\u00e0 degli Studi di Bari Aldo Moro, 70121 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1372-3916","authenticated-orcid":false,"given":"Sabina","family":"Tangaro","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70121 Bari, Italy"},{"name":"Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universit\u00e0 degli Studi di Bari Aldo Moro, 70121 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Bellotti","sequence":"additional","affiliation":[{"name":"Dipartimento Interateneo di Fisica \u201cM. Merlin\u201d, Universit\u00e0 degli Studi di Bari Aldo Moro, 70121 Bari, Italy"},{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70121 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2009.08.011","article-title":"Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States","volume":"114","author":"Roy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_2","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 sen2cor: L2a processor for users. Proceedings of the Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0034-4257(02)00089-5","article-title":"Atmospheric correction of MODIS data in the visible to middle infrared: First results","volume":"83","author":"Vermote","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","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_9","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_10","doi-asserted-by":"crossref","first-page":"2713","DOI":"10.5194\/amt-6-2713-2013","article-title":"A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI","volume":"6","author":"Bley","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.rse.2018.08.009","article-title":"A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses","volume":"217","author":"Coluzzi","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.rse.2010.03.002","article-title":"A multi-temporal method for cloud detection, applied to FORMOSAT-2, VEN\u03bcS, LANDSAT and SENTINEL-2 images","volume":"114","author":"Hagolle","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mateo-Garc\u00eda, G., G\u00f3mez-Chova, L., Amor\u00f3s-L\u00f3pez, J., Mu\u00f1oz-Mar\u00ed, J., and Camps-Valls, G. (2018). Multitemporal cloud masking in the Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10071079"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2013.11.001","article-title":"Contextual classification of lidar data and building object detection in urban areas","volume":"87","author":"Niemeyer","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shao, Z., Deng, J., Wang, L., Fan, Y., Sumari, N., and Cheng, Q. (2017). Fuzzy autoencode based cloud detection for remote sensing imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040311"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2019.08.018","article-title":"Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery","volume":"157","author":"Shendryk","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1109\/TGRS.2002.808301","article-title":"The MODIS cloud products: Algorithms and examples from Terra","volume":"41","author":"Platnick","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sanchez, A.H., Picoli, M.C.A., Camara, G., Andrade, P.R., Chaves, M.E.D., Lechler, S., Soares, A.R., Marujo, R.F.B., Sim\u00f5es, R.E.O., and Ferreira, K.R. (2020). Comparison of Cloud Cover Detection Algorithms on Sentinel-2 Images of the Amazon Tropical Forest. Remote Sens., 12.","DOI":"10.3390\/rs12081284"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jneumeth.2017.12.011","article-title":"Deep learning reveals Alzheimer\u2019s disease onset in MCI subjects: Results from an international challenge","volume":"302","author":"Amoroso","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1038\/s41592-019-0509-5","article-title":"Assessment of network module identification across complex diseases","volume":"16","author":"Choobdar","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hollstein, A., Segl, K., Guanter, L., Brell, M., and Enesco, M. (2016). Ready-to-use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images. Remote Sens., 8.","DOI":"10.3390\/rs8080666"},{"key":"ref_24","unstructured":"(2019, August 24). Available Data. Available online: https:\/\/github.com\/hollstein\/cB4S2."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_26","unstructured":"Baetens, L., and Hagolle, O. (2018). Sentinel-2 reference cloud masks generated by an active learning method. Zenodo."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gao, B.C., Goetz, A.F.H., and Wiscombe, W.J. (1993). Cirrus cloud detection from Airborne Imaging Spectrometer data using the 1.38 \u03bcm water vapor band. Geophys. Res. Lett., 20.","DOI":"10.1029\/93GL00106"},{"key":"ref_28","unstructured":"(1997). USGS 30 ARC-Second Global Elevation Data, GTOPO30, NCAR Computational and Information Systems Laboratory."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/0034-4257(89)90101-6","article-title":"Spectral signature of alpine snow cover from the Landsat Thematic Mapper","volume":"28","author":"Dozier","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"1","article-title":"Sentinel-2 msi\u2013level 2a products algorithm theoretical basis document","volume":"49","author":"Richter","year":"2012","journal-title":"Eur. Space Agency (Spec. Publ.) ESA SP"},{"key":"ref_31","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_32","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_33","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."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lyapustin, A., Wang, Y., and Frey, R. (2008). An automatic cloud mask algorithm based on time series of MODIS measurements. J. Geophys. Res. Atmos., 113, Available online: https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1029\/2007JD009641.","DOI":"10.1029\/2007JD009641"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_38","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_40","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_41","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2019). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, R Foundation for Statistical Computing. R package version 1.7-3."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Le Cun, Y. (1986). Learning process in an asymmetric threshold network. Disordered Systems and Biological Organization, Springer.","DOI":"10.1007\/978-3-642-82657-3_24"},{"key":"ref_43","unstructured":"Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. Neural Networks for Perception, Elsevier."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_45","unstructured":"LeDell, E., Gill, N., Aiello, S., Fu, A., Candel, A., Click, C., Kraljevic, T., Nykodym, T., Aboyoun, P., and Kurka, M. (2020). h2o: R Interface for the \u2019H2O\u2019 Scalable Machine Learning Platform, R Foundation for Statistical Computing. R package version 3.28.0.4."},{"key":"ref_46","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1088\/1361-6560\/aa5dbe","article-title":"DTI measurements for Alzheimer\u2019s classification","volume":"62","author":"Maggipinto","year":"2017","journal-title":"Phys. Med. Biol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1214\/aoms\/1177730491","article-title":"On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other","volume":"18","author":"Mann","year":"1947","journal-title":"Ann. Math. Stat."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1080\/01621459.1974.10482955","article-title":"Robust Tests for the Equality of Variances","volume":"69","author":"Brown","year":"1974","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual Comparisons by Ranking Methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biom. Bull."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"042609","DOI":"10.1117\/1.JRS.11.042609","article-title":"Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1177\/1475921718798622","article-title":"Wavelet packet energy\u2013based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory","volume":"18","author":"Yu","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1177\/1475921705049747","article-title":"Quantitative damage prediction for composite laminates based on wave propagation and artificial neural networks","volume":"4","author":"Su","year":"2005","journal-title":"Struct. Health Monit."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1038\/nn.2303","article-title":"Circular analysis in systems neuroscience: The dangers of double dipping","volume":"12","author":"Kriegeskorte","year":"2009","journal-title":"Nat. Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/BF00993277","article-title":"Improving generalization with active learning","volume":"15","author":"Cohn","year":"1994","journal-title":"Mach. Learn."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"7776","DOI":"10.1109\/ACCESS.2017.2696365","article-title":"Machine learning with big data: Challenges and approaches","volume":"5","author":"Grolinger","year":"2017","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_59","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_60","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_61","doi-asserted-by":"crossref","unstructured":"Yang, X., Jia, Z., Yang, J., and Kasabov, N. (2019). Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm. Sensors, 19.","DOI":"10.3390\/s19091972"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2355\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:52Z","timestamp":1760176252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,22]]},"references-count":61,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12152355"],"URL":"https:\/\/doi.org\/10.3390\/rs12152355","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,22]]}}}