{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T18:05:03Z","timestamp":1775153103667,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T00:00:00Z","timestamp":1658361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["4196103"],"award-info":[{"award-number":["4196103"]}]},{"name":"National Natural Science Foundation of China","award":["202201AT070164"],"award-info":[{"award-number":["202201AT070164"]}]},{"name":"National Natural Science Foundation of China","award":["202101AT070102"],"award-info":[{"award-number":["202101AT070102"]}]},{"name":"Yunnan Fundamental Research Projects","award":["4196103"],"award-info":[{"award-number":["4196103"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202201AT070164"],"award-info":[{"award-number":["202201AT070164"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202101AT070102"],"award-info":[{"award-number":["202101AT070102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper proposes a superpixel spatial intuitionistic fuzzy C-means (SSIFCM) clustering algorithm to address the problems of misclassification, salt and pepper noise, and classification uncertainty arising in the pixel-level unsupervised classification of high spatial resolution remote sensing (HSRRS) images. To reduce information redundancy and ensure noise immunity and image detail preservation, we first use a superpixel segmentation to obtain the local spatial information of the HSRRS image. Secondly, based on the bias-corrected fuzzy C-means (BCFCM) clustering algorithm, the superpixel spatial intuitionistic fuzzy membership matrix is constructed by counting an intuitionistic fuzzy set and spatial function. Finally, to minimize the classification uncertainty, the local relation between adjacent superpixels is used to obtain the classification results according to the spectral features of superpixels. Four HSRRS images of different scenes in the aerial image dataset (AID) are selected to analyze the classification performance, and fifteen main existing unsupervised classification algorithms are used to make inter-comparisons with the proposed SSIFCM algorithm. The results show that the overall accuracy and Kappa coefficients obtained by the proposed SSIFCM algorithm are the best within the inter-comparison of fifteen algorithms, which indicates that the SSIFCM algorithm can effectively improve the classification accuracy of HSRRS image.<\/jats:p>","DOI":"10.3390\/rs14143490","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T22:38:50Z","timestamp":1658443130000},"page":"3490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Superpixel Spatial Intuitionistic Fuzzy C-Means Clustering Algorithm for Unsupervised Classification of High Spatial Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Xinran","family":"Ji","sequence":"first","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6667-759X","authenticated-orcid":false,"given":"Liang","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"},{"name":"Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1918-5346","authenticated-orcid":false,"given":"Bo-Hui","family":"Tang","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"},{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5866-6321","authenticated-orcid":false,"given":"Guokun","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feifei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.11834\/jrs.20210260","article-title":"Application-Oriented Real-Time Remote Sensing Service Technology","volume":"25","author":"Li","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6513","DOI":"10.1007\/s00521-019-04046-7","article-title":"Sparsity-Regularized Feature Selection for Multi-class Remote Sensing Image Classification","volume":"32","author":"Chen","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/S0165-0114(99)80004-9","article-title":"Fuzzy Sets as a Basis for a Theory of Possibility","volume":"100","author":"Zadeh","year":"1999","journal-title":"Fuzzy Sets Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5057","DOI":"10.1109\/TGRS.2017.2702061","article-title":"A Novel Adaptive Fuzzy Local Information C-Means Clustering Algorithm for Remotely Sensed Imagery Classification","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/JAS.2020.1003420","article-title":"Residual-Driven Fuzzy C-Means Clustering for Image Segmentation","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE CAA J. Autom. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s102080010027","article-title":"Adaptive wavelet methods II\u2014Beyond the elliptic case","volume":"2","author":"Cohen","year":"2002","journal-title":"Found. Comput. Math."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cherfa, I., Mokraoui, A., and Mekhmoukh, A. (2020, January 23\u201325). Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering Algorithm Using Particle Swarm Optimization for Medical Image Segmentation. Proceedings of the 24th IEEE Conference on Signal Processing: Algorithms, Architectures, Arrangements, and Applications (IEEE SPA), Electr Network.","DOI":"10.23919\/SPA50552.2020.9241242"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106200","DOI":"10.1016\/j.asoc.2020.106200","article-title":"Local Segmentation of Images Using an Improved Fuzzy C-Means Clustering Algorithm Based on Self-adaptive Dictionary Learning","volume":"91","author":"Miao","year":"2020","journal-title":"Appl. Soft Comput. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"113856","DOI":"10.1016\/j.eswa.2020.113856","article-title":"Fuzzy C-Means Clustering Algorithm for Data with Unequal Cluster Sizes and Contaminated with Noise and Outliers: Review and Development","volume":"165","author":"Askari","year":"2020","journal-title":"Expert Sys. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.ins.2020.10.003","article-title":"A Hybrid Interval Type-2 Semi-Supervised Possibilistic Fuzzy C-Means Clustering and Particle Swarm Optimization for Satellite Image Analysis","volume":"548","author":"Mai","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"580","DOI":"10.3969\/j.issn.1004-4132.2010.04.009","article-title":"Intuitionistic Fuzzy C-Means Clustering Algorithms","volume":"4","author":"Xu","year":"2010","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2725186","DOI":"10.1155\/2020\/2725186","article-title":"Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering","volume":"2020","author":"Huang","year":"2020","journal-title":"J. Spectrosc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1109\/TFUZZ.2019.2917809","article-title":"Distribution Information Based Intuitionistic Fuzzy Clustering for Infrared Ship Segmentation","volume":"28","author":"Jin","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ren, X.F., and Malik, J. (2003, January 13\u201316). Learning a Classification Model for Segmentation. Proceedings of the IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.image.2017.04.007","article-title":"Superpixel Segmentation: A Benchmark","volume":"56","author":"Wang","year":"2017","journal-title":"Signal Process Image Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1109\/TFUZZ.2018.2889018","article-title":"Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation","volume":"27","author":"Lei","year":"2018","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3027","DOI":"10.1109\/TFUZZ.2018.2796074","article-title":"Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering","volume":"26","author":"Lei","year":"2018","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1049\/iet-ipr.2019.0255","article-title":"Image Classification Using SLIC Superpixel and FAAGKFCM Image Segmentation","volume":"14","author":"Singh","year":"2020","journal-title":"IET Image Proc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4132","DOI":"10.1049\/iet-ipr.2020.0402","article-title":"Image Clustering Algorithm Using Superpixel Segmentation and Non-Symmetric Gaussian\u2013Cauchy Mixture Model","volume":"14","author":"Ji","year":"2020","journal-title":"IET Image Proc."},{"key":"ref_21","first-page":"589","article-title":"Superpixel Segmentation Method of High-Resolution Remote Sensing Image Based on Fuzzy Clustering","volume":"49","author":"Huang","year":"2020","journal-title":"Cehui Xuebao"},{"key":"ref_22","first-page":"263","article-title":"Superpixel Segmentation Method of High Resolution Remote Sensing Images Based on Hierarchical Clustering","volume":"39","author":"Huang","year":"2020","journal-title":"Hongwai Yu Haomibo Xuebao"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/42.996338","article-title":"A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data","volume":"21","author":"Ahmed","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0165-0114(86)80034-3","article-title":"Intuitionistic Fuzzy Sets","volume":"20","author":"Atanassov","year":"1986","journal-title":"Fuzzy Sets Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1109\/TFUZZ.2006.890678","article-title":"Intuitionistic Fuzzy Aggregation Operators","volume":"15","author":"Xu","year":"2007","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/S0019-9958(80)90156-4","article-title":"On the Measures of Fuzziness and Negation Part II Lattices","volume":"44","author":"Yager","year":"1980","journal-title":"Inf. Control"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sugeno, M. (1993). Fuzzy Measures and Fuzzy Integrals\u2014A Survey. Readings in Fuzzy Sets for Intelligent Systems, Elsevier.","DOI":"10.1016\/B978-1-4832-1450-4.50027-4"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1016\/j.rse.2007.07.017","article-title":"Sub-Pixel Confusion-Uncertainty Matrix for Assessing Soft Classifications","volume":"112","author":"Wang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tripathy, B.K., Basan, A., and Govel, S. (2014, January 18\u201320). Image segmentation using spatial intuitionistic fuzzy C means clustering. Proceedings of the 5th IEEE International Conference on Computational Intelligence and Computing Research (IEEE ICCIC), Park Coll Engn & Tekhnol, Coimbatore, India.","DOI":"10.1109\/ICCIC.2014.7238446"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TPAMI.2002.1017616","article-title":"An Efficient K-Means Clustering Algorithm: Analysis and Implementation","volume":"24","author":"Kanungo","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0016-7061(97)00019-0","article-title":"Fuzzy and Isodata Classification of Landform Elements from Digital Terrain Data in Pleasant Valley, Wisconsin","volume":"77","author":"Irvin","year":"1997","journal-title":"Geoderma"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1093\/bioinformatics\/btg119","article-title":"Fuzzy C-means method for clustering microarray data","volume":"19","author":"Dembele","year":"2003","journal-title":"Bioinformatics"},{"key":"ref_36","unstructured":"Kim, T.H., Park, D.C., Woo, D.M., Han, S.S., and Lee, Y. (2011, January 10\u201311). MRI Image Segmentation Using Intuitive Fuzzy C-Means Algorithm. Proceedings of the 2011 International Conference on Computer, Electrical, and Systems Sciences, and Engineering (CESSE 2011), Wuhan, China."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TIP.2010.2040763","article-title":"A Robust Fuzzy Local Information C-Means Clustering Algorithm","volume":"19","author":"Krinidis","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TFUZZ.2008.2005008","article-title":"A Fuzzy Clustering Approach toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation","volume":"16","author":"Chatzis","year":"2008","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TSMCB.2004.831165","article-title":"Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-Induced Distance Measure","volume":"34","author":"Chen","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106318","DOI":"10.1016\/j.asoc.2020.106318","article-title":"Robust Fuzzy C-means Clustering Algorithm with Adaptive Spatial & Intensity Constraint and Membership Linking for Noise Image Segmentation","volume":"92","author":"Wang","year":"2020","journal-title":"Appl. Soft Comput. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/TCSVT.2020.3019109","article-title":"Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering","volume":"31","author":"Wu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kanezaki, A. (2018, January 15\u201320). Unsupervised Image Segmentation by Backpropagation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada.","DOI":"10.1109\/ICASSP.2018.8462533"},{"key":"ref_43","unstructured":"(2019, June 19). Unsupervised-Segmentation. Available online: https:\/\/github.com\/Yonv1943\/Unsupervised-Segmentation\/tree\/master."},{"key":"ref_44","first-page":"409","article-title":"Unsupervised Classification of High Spectral Resolution Images Using the Kohonen Self-Organization Neural Network","volume":"13","author":"Guo","year":"1994","journal-title":"Hongwai Yu Haomibo Xuebao"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3490\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:29Z","timestamp":1760140529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,21]]},"references-count":44,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143490"],"URL":"https:\/\/doi.org\/10.3390\/rs14143490","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,21]]}}}