{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T15:52:46Z","timestamp":1776613966626,"version":"3.51.2"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Project of National Nature Science Foundation of China","award":["41331175"],"award-info":[{"award-number":["41331175"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable image classification results are crucial for the application of remote sensing images, but the reliability of image classification has received less attention. In particular, the inherent uncertainty of remote sensing images has been disregarded. The uncertainty of a remote sensing image accumulates and propagates continuously in the classification process and ultimately affects the reliability of the classification results. Therefore, quantitative description and investigation of the inherent uncertainty of remote sensing images are crucial in achieving reliable remote sensing image classification. In this study, we analyze the sources of uncertainty of remote sensing images in detail and propose a quantitative descriptor for measuring image uncertainty comprehensively and effectively. In addition, we also design two verification schemes to verify the validity of the proposed uncertainty descriptor. Finally, the validity of the proposed uncertainty descriptor is confirmed by experimental results on three real remote sensing images. Our study on the uncertainty of remote sensing images may help the development of uncertainty control methods and reliable classification schemes of remote sensing images.<\/jats:p>","DOI":"10.3390\/rs11131560","type":"journal-article","created":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T10:54:47Z","timestamp":1561978487000},"page":"1560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Uncertainty Descriptor for Quantitative Measurement of the Uncertainty of Remote Sensing Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9113-1350","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1821-538X","authenticated-orcid":false,"given":"Penglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,1]]},"reference":[{"key":"ref_1","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Plenum Publishing Co."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/JPROC.2012.2211551","article-title":"Land-Cover Mapping by Markov Modeling of Spatial\u2013Contextual Information in Very-High-Resolution Remote Sensing Images","volume":"101","author":"Moser","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/LGRS.2013.2294241","article-title":"Supervised Segmentation of Very High Resolution Images by the Use of Extended Morphological Attribute Profiles and a Sparse Transform","volume":"11","author":"Li","year":"2014","journal-title":"IEEE Geosci. Remote. Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.isprsjprs.2017.10.006","article-title":"Spectral-spatial classification of hyperspectral imagery with cooperative game","volume":"135","author":"Zhao","year":"2018","journal-title":"ISPRS J. Photogramm."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE T Geosci. Remote Sens."},{"key":"ref_6","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."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4871","DOI":"10.1080\/0143116031000070490","article-title":"Classification of wheat crop with multi-temporal images: Performance of maximum likelihood and artificial neural networks","volume":"24","author":"Murthy","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2008.916090","article-title":"Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle","volume":"46","author":"Blanzieri","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2010.09.007","article-title":"A fuzzy topology-based maximum likelihood classification","volume":"66","author":"Liu","year":"2011","journal-title":"Isprs J Photogramm"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ying, L., and Bo, C. (2009, January 12\u201314). An improved k-nearest neighbor algorithm and its application to high resolution remote sensing image classification. Proceedings of the 2009 17th International Conference on Geoinformatics, Fairfax, VA, USA.","DOI":"10.1109\/GEOINFORMATICS.2009.5293389"},{"key":"ref_11","first-page":"1","article-title":"Modified algorithm based on support vector machines for classification of hyperspectral images in a similarity space","volume":"6","author":"Hosseini","year":"2012","journal-title":"J. Appl. Rem. Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J Photogramm."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1109\/LGRS.2013.2262132","article-title":"Object-Based Spatial Feature for Classification of Very High Resolution Remote Sensing Images","volume":"10","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","article-title":"Hyperspectral Image Classification Using Dictionary-Based Sparse Representation","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE T Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6114","DOI":"10.1109\/TGRS.2015.2432059","article-title":"Simultaneous Sparse Graph Embedding for Hyperspectral Image Classification","volume":"53","author":"Xue","year":"2015","journal-title":"IEEE T Geosci. Remote Sens."},{"key":"ref_16","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."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/LGRS.2013.2254108","article-title":"Hyperspectral Remote Sensing Image Classification Based on Rotation Forest","volume":"11","author":"Xia","year":"2014","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/LGRS.2004.823453","article-title":"Controlling data uncertainty via aggregation in remotely sensed data","volume":"1","author":"Carmel","year":"2004","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Choi, M., Lee, H., and Lee, S. (2016, January 25\u201328). Weighted SVM with classification uncertainty for small training samples. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533199"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.1109\/TGRS.2011.2121916","article-title":"A Markov Chain Geostatistical Framework for Land-Cover Classification With Uncertainty Assessment Based on Expert-Interpreted Pixels From Remotely Sensed Imagery","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3769","DOI":"10.1109\/TGRS.2010.2047863","article-title":"Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs","volume":"48","author":"Giacco","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/LGRS.2017.2763979","article-title":"A Novel Approach of Fuzzy Dempster\u2013Shafer Theory for Spatial Uncertainty Analysis and Accuracy Assessment of Object-Based Image Classification","volume":"15","author":"Feizizadeh","year":"2018","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, X., Hao, M., Shao, P., Cai, L., and Lyu, X. (2015). Validation of Land Cover Products Using Reliability Evaluation Methods. Remote Sens., 7.","DOI":"10.3390\/rs70607846"},{"key":"ref_25","first-page":"488","article-title":"The Uncertainty Principle in Image Processing","volume":"17","author":"Wilson","year":"1995","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gillmann, C., Arbelaez, P., Hernandez, T.J., Hagen, H., and Wischgoll, T. (2018). An Uncertainty-Aware Visual System for Image Pre-Processing. J. Imaging, 4.","DOI":"10.3390\/jimaging4090109"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gillmann, C., Post, T., Wischgoll, T., Hagen, H., and Maciejewski, R. (2019). Hierarchical Image Semantics using Probabilistic Path Propagations for Biomedical Research. IEEE Comput. Graph. Appl., 1.","DOI":"10.1109\/MCG.2019.2894094"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shi, W.Z. (2008). Principles of Modelling Uncertainties in Spatial Data and Spatial Analyses, CRC Press.","DOI":"10.1201\/9781420059281"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TGRS.2009.2024302","article-title":"A Fuzzy-Topology-Based Area Object Extraction Method","volume":"48","author":"Shi","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","unstructured":"Dumoulin, V., and Visin, F. (2019, May 06). A guide to convolution arithmetic for deep learning. Available online: https:\/\/arxiv.org\/abs\/1603.07285."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/0952813X.2014.924585","article-title":"ABC-based distance-weighted kNN algorithm","volume":"27","author":"Yigit","year":"2015","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1081\/STA-120002859","article-title":"Modelling the coefficient of variation in factorial experiments","volume":"31","author":"Wilson","year":"2002","journal-title":"Commun. Stat.-Theory Methods"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.eswa.2018.04.008","article-title":"Dynamic selection of normalization techniques using data complexity measures","volume":"106","author":"Jain","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1214\/ss\/1177012580","article-title":"Francis Galton's Account of the Invention of Correlation","volume":"4","author":"Stigler","year":"1989","journal-title":"Statist. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TGRS.2014.2308192","article-title":"New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study","volume":"52","author":"Huang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cui, G., Lv, Z., Li, G., Atli Benediktsson, J., and Lu, Y. (2018). Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10081238"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Economic Geography"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lv, Z., Zhang, P., and Atli Benediktsson, J. (2017). Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler\u2019s First Law of Geography for Very High Resolution Aerial Imagery Classification. Remote Sens., 9.","DOI":"10.20944\/preprints201703.0134.v1"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/j.1538-7305.1948.tb00917.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"293","DOI":"10.5194\/isprsannals-I-3-293-2012","article-title":"The ISPRS benchmark on urban object classification and 3D building reconstruction","volume":"I\u20133","author":"Rottensteiner","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1560\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:02:54Z","timestamp":1760187774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,1]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131560"],"URL":"https:\/\/doi.org\/10.3390\/rs11131560","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,1]]}}}