{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:49:17Z","timestamp":1792514957953,"version":"4.5.0"},"reference-count":52,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071319"],"award-info":[{"award-number":["42071319"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The high spectral resolution of hyperspectral images (HSIs) provides rich information but causes data redundancy, which imposes a computational burden on practical applications. Band selection methods can select a subset of HSI without changing the main information to reduce the spectral dimension. Clustering-based methods can reduce band correlation significantly, but traditional clustering methods are mostly hard clustering and are not accurate enough to partition the bands. An unsupervised band selection method based on fuzzy c-means clustering (FCM) was introduced to tackle this problem. However, FCM can easily obtain the local optimal solution and take a long time to process high-dimensional data. Hence, this work applies kernel function and a sampling strategy to reduce calculation time, and information entropy is used to initialize the cluster center. A kernel FCM algorithm based on spatial sampling and a grouping information entropy strategy is proposed and called SSGIE-KFCM. This method not only optimizes the calculation process and reduces the amount of computation data, accelerating the calculation efficiency, but also adopts grouping information entropy to improve the probability of obtaining a global optimal solution. Classification experiments on two public HSI datasets show that: (1) The classification performance of the whole band can be achieved or even exceeded by using only a small number of bands to achieve the purpose of dimensionality reduction. (2) The classification accuracy can be improved compared with the FCM method. (3) With the introduction of sampling strategy and kernel function, the computational speed is at least 24 times faster than that of FCM. It has been proven that the SSGIE-KFCM method can significantly reduce the amount of HSI while retaining the primary information of the original data, which further promotes the research and application of HSI in the remote sensing area.<\/jats:p>","DOI":"10.3390\/rs14195058","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:50:01Z","timestamp":1665449401000},"page":"5058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Spatial Sampling and Grouping Information Entropy Strategy Based on Kernel Fuzzy C-Means Clustering Method for Hyperspectral Band Selection"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhou","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7524-1392","authenticated-orcid":false,"given":"Degang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5389-7251","authenticated-orcid":false,"given":"Xu","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, East China University of Technology, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Ni","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,10]]},"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 Observ. Remote Sens."},{"key":"ref_2","first-page":"102926","article-title":"Deep learning in multimodal remote sensing data fusion: A comprehensive review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_3","unstructured":"Zhuang, L., and Ng, M.K. (2021). Fasthymix: Fast and parameter-free hyperspectral image mixed noise removal. IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_4","first-page":"6007305","article-title":"Deep unsupervised blind hyperspectral and multispectral data fusion","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_6","first-page":"6004905","article-title":"A global-local spectral weight network based on attention for hyperspectral band selection","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Agarwal, A., El-Ghazawi, T., El-Askary, H., and Le-Moigne, J. (2007, January 15\u201318). Efficient hierarchical-pca dimension reduction for hyperspectral imagery. Proceedings of the 2007 IEEE International Symposium on Signal Processing and Information Technology, Giza, Egypt.","DOI":"10.1109\/ISSPIT.2007.4458191"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TGRS.2005.863297","article-title":"Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis","volume":"44","author":"Jing","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jia, S., Qian, Y., Li, J., Liu, W., and Ji, Z. (2010, January 25\u201330). Feature extraction and selection hybrid algorithm for hyperspectral imagery classification. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652463"},{"key":"ref_10","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":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5518615","DOI":"10.1109\/TGRS.2021.3130716","article-title":"Spectralformer: Rethinking hyperspectral image classification with transformers","volume":"60","author":"Hong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fu, X., Shang, X., Sun, X., Yu, H., Song, M., and Chang, C.-I. (2020). Underwater hyperspectral target detection with band selection. Remote Sens., 12.","DOI":"10.3390\/rs12071056"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.1109\/TGRS.2020.3024852","article-title":"Target detection through tree-structured encoding for hyperspectral images","volume":"59","author":"Sun","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"5508123","article-title":"Ensemble-based information retrieval with mass estimation for hyperspectral target detection","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5526419","DOI":"10.1109\/TGRS.2022.3163173","article-title":"Siamese transformer network for hyperspectral image target detection","volume":"60","author":"Rao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","first-page":"102669","article-title":"Transferable network with siamese architecture for anomaly detection in hyperspectral images","volume":"106","author":"Rao","year":"2022","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_17","first-page":"5500117","article-title":"Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations","volume":"60","author":"Zhuang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, M., Wang, Q., Hong, D., Roy, S.K., and Chanussot, J. (2022). Learning tensor low-rank representation for hyperspectral anomaly detection. IEEE Trans. Cybern.","DOI":"10.1109\/TCYB.2022.3175771"},{"key":"ref_19","first-page":"5504017","article-title":"Using low-rank representation of abundance maps and nonnegative tensor factorization for hyperspectral nonlinear unmixing","volume":"60","author":"Gao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5524913","DOI":"10.1109\/TGRS.2022.3155794","article-title":"Multimodal hyperspectral unmixing: Insights from attention networks","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhuang, L., Ng, M.K., and Fu, X. (2021). Hyperspectral image mixed noise removal using subspace representation and deep cnn image prior. Remote Sens., 13.","DOI":"10.3390\/rs13204098"},{"key":"ref_22","first-page":"5511413","article-title":"Adaptive hyperspectral mixed noise removal","volume":"60","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"5531617","article-title":"Nonlocal self-similarity-based hyperspectral remote sensing image denoising with 3-d convolutional neural network","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/JSTARS.2020.2979801","article-title":"Hyperspectral mixed noise removal by l1-norm-based subspace representation","volume":"13","author":"Zhuang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_25","first-page":"5533216","article-title":"Generalized linear spectral mixing model for spatial\u2013temporal\u2013spectral fusion","volume":"60","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5522914","DOI":"10.1109\/TGRS.2022.3146296","article-title":"Mlr-dbpfn: A multi-scale low rank deep back projection fusion network for anti-noise hyperspectral and multispectral image fusion","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5515815","DOI":"10.1109\/TGRS.2021.3102136","article-title":"Hy-demosaicing: Hyperspectral blind reconstruction from spectral subsampling","volume":"60","author":"Zhuang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"5513712","article-title":"A multiscale spectral features graph fusion method for hyperspectral band selection","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5527613","DOI":"10.1109\/TGRS.2022.3169018","article-title":"A dual global\u2013local attention network for hyperspectral band selection","volume":"60","author":"He","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/LGRS.2017.2681118","article-title":"Unsupervised hyperspectral band selection by fuzzy clustering with particle swarm optimization","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"Fcm: The fuzzy c-means clustering algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Comput. Geosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TFUZZ.2012.2201485","article-title":"Fuzzy c-means algorithms for very large data","volume":"20","author":"Havens","year":"2012","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, S., and Qi, H. (2011, January 11\u201314). Sparse representation based band selection for hyperspectral images. Proceedings of the 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6116223"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1631\/jzus.C1000304","article-title":"Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization","volume":"12","author":"Li","year":"2011","journal-title":"J. Zhejiang Univ. Sci. C"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1109\/JSTARS.2015.2417156","article-title":"Band selection using improved sparse subspace clustering for hyperspectral imagery classification","volume":"8","author":"Sun","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1109\/T-C.1971.223410","article-title":"A direct method of nonparametric measurement selection","volume":"C-20","author":"Whitney","year":"1971","journal-title":"IEEE Trans. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1016\/S0167-8655(99)00083-5","article-title":"Adaptive floating search methods in feature selection","volume":"20","author":"Somol","year":"1999","journal-title":"Pattern Recognit. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2015.2497085","article-title":"Hyperspectral band selection using improved firefly algorithm","volume":"13","author":"Su","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/TGRS.2015.2461653","article-title":"Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images","volume":"54","author":"Gong","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1109\/36.803411","article-title":"A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification","volume":"37","author":"Chang","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"053101","DOI":"10.1117\/1.OE.56.5.053101","article-title":"Covariance-based band selection and its application to near-real-time hyperspectral target detection","volume":"56","author":"Kim","year":"2017","journal-title":"Opt. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.1109\/TGRS.2007.904951","article-title":"Clustering-based hyperspectral band selection using information measures","volume":"45","author":"Pla","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1049\/iet-cvi.2009.0034","article-title":"Band selection for hyperspectral imagery using affinity propagation","volume":"3","author":"Qian","year":"2009","journal-title":"IET Comput. Vis."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2656","DOI":"10.1364\/AO.51.002656","article-title":"Adaptive affinity propagation with spectral angle mapper for semi-supervised hyperspectral band selection","volume":"51","author":"Su","year":"2012","journal-title":"Appl. Opt."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.jvcir.2017.02.002","article-title":"Representative band selection for hyperspectral image classification","volume":"48","author":"Yang","year":"2017","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1109\/TGRS.2015.2480866","article-title":"Dual-clustering-based hyperspectral band selection by contextual analysis","volume":"54","author":"Yuan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.patcog.2007.05.018","article-title":"A survey of kernel and spectral methods for clustering","volume":"41","author":"Filippone","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, S., Peng, B., Fang, L., and Li, Q. (2022). Hyperspectral band selection via optimal combination strategy. Remote Sens., 14.","DOI":"10.3390\/rs14122858"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4869","DOI":"10.1109\/JSTARS.2020.3018229","article-title":"Efficient graph convolutional self-representation for band selection of hyperspectral image","volume":"13","author":"Cai","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4318","DOI":"10.1109\/TGRS.2018.2811046","article-title":"A geometry-based band selection approach for hyperspectral image analysis","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:49:27Z","timestamp":1760143767000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":52,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14195058"],"URL":"https:\/\/doi.org\/10.3390\/rs14195058","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,10]]}}}