{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T01:18:29Z","timestamp":1768785509494,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council (Taiwan)","award":["NSTC 111-2221-E-110-030-MY2"],"award-info":[{"award-number":["NSTC 111-2221-E-110-030-MY2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Band subset selection (BSS) is one of the ways to implement band selection (BS) for a hyperspectral image (HSI). Different from conventional BS methods, which select bands one by one, BSS selects a band subset each time and preserves the best one from the collection of the band subsets. This paper proposes a BSS method, called band grouping-based sparse self-representation BSS (BG-SSRBSS), for hyperspectral image classification. It formulates BS as a sparse self-representation (SSR) problem in which the entire bands can be represented by a set of informatively complementary bands. The BG-SSRBSS consists of two steps. To tackle the issue of selecting redundant bands, it first applies band grouping (BG) techniques to pre-group the entire bands to form multiple band groups, and then performs band group subset selection (BGSS) to find the optimal band group subset. The corresponding representative bands are taken as the BS result. To efficiently find the nearly global optimal subset among all possible band group subsets, sequential and successive iterative search algorithms are adopted. Land cover classification experiments conducted on three real HSI datasets show that BG-SSRBSS can improve classification accuracy by 4\u201320% compared to the existing BSS methods and requires less computation time.<\/jats:p>","DOI":"10.3390\/rs14225686","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T21:33:02Z","timestamp":1668115982000},"page":"5686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Band Subset Selection Approach Based on Sparse Self-Representation and Band Grouping for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9358-6511","authenticated-orcid":false,"given":"Keng-Hao","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan"}]},{"given":"Yu-Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan"}]},{"given":"Tsun-Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral Band Selection: A Review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MGRS.2021.3051979","article-title":"A Review of Unsupervised Band Selection Techniques: Land Cover Classification for Hyperspectral Earth Observation Data","volume":"9","author":"Patro","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1109\/TGRS.2006.864389","article-title":"Constrained band selection for hyperspectral imagery","volume":"44","author":"Chang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/LGRS.2005.844658","article-title":"Band selection based on feature weighting for classification of hyperspectral data","volume":"2","author":"Huang","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1109\/TGRS.2013.2257604","article-title":"Progressive Band Selection of Spectral Unmixing for Hyperspectral Imagery","volume":"52","author":"Chang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/LGRS.2008.2000619","article-title":"Similarity-based unsupervised band selection for Hyperspectral Image Analysis","volume":"5","author":"Du","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","first-page":"935","article-title":"A New Dimensionality Reduction Algorithm for Hyperspectral Image Using Evolutionary Strategy, IEEE Trans","volume":"8","author":"Yin","year":"2012","journal-title":"Ind. Informat."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4092","DOI":"10.1109\/TGRS.2013.2279591","article-title":"Hyperspectral band selection based on trivariate mutual information and clonal selection","volume":"52","author":"Feng","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.1109\/JSTARS.2014.2312539","article-title":"Optimized Hyperspectral Band Selection Using Particle Swarm Optimization","volume":"7","author":"Su","year":"2014","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TGRS.2014.2367010","article-title":"A Novel Feature Selection Approach Based on FODPSO and SVM","volume":"53","author":"Ghamisi","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.asoc.2015.09.045","article-title":"Gray Wolf Optimizer for Hyperspectral Band Selection","volume":"40","author":"Medjahed","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1109\/TIP.2017.2676344","article-title":"Band Selection for Nonlinear Unmixing of Hyperspectral Images as a Maximal Clique Problem","volume":"26","author":"Imbiriba","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/LGRS.2015.2404772","article-title":"Unsupervised Hyperspectral Image Band Selection via Column Subset Selection","volume":"12","author":"Wang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1109\/LGRS.2017.2749209","article-title":"Constrained Band Subset Selection for Hyperspectral Imagery","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4630","DOI":"10.1109\/JSTARS.2017.2724604","article-title":"Channel Capacity Approach to Hyperspectral Band Subset Selection","volume":"10","author":"Chang","year":"2017","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yu, C., Song, M., and Chang, C.-I. (2018). Band Subset Selection for Hyperspectral Image Classification. Remote Sens., 10.","DOI":"10.3390\/rs10010113"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TGRS.2015.2453362","article-title":"Unsupervised hyperspectral band selection by dominant set extraction","volume":"54","author":"Zhu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, C., Tan, Y., Bruzzone, L., Lu, L., and Guan, R. (2017). Discriminative feature metric learning in the affinity propagation model for band selection in hyperspectral images. Remote Sens., 9.","DOI":"10.3390\/rs9080782"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TIP.2016.2617462","article-title":"Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection","volume":"26","author":"Yuan","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1109\/LGRS.2019.2912170","article-title":"Unsupervised Hyperspectral Image Band Selection Based on Deep Subspace Clustering","volume":"16","author":"Zeng","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","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 18th IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6116223"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, Y., Duan, J., Xiang, S., and Pan, C. (2013, January 15\u201318). Group sparsitybased semi-supervised band selection for hyperspectral images. Proceedings of the IEEE International Conference on Image Processing, Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738664"},{"key":"ref_26","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. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lai, C.-H., Chen, C.-S., Chen, S.-Y., and Liu, K.-H. (2016, January 21\u201324). Sequential band selection method based on group orthogonal matching pursuit. Proceedings of the 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071779"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4374","DOI":"10.1109\/JSTARS.2016.2539981","article-title":"A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification","volume":"9","author":"Sun","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5087","DOI":"10.1109\/JSTARS.2017.2737400","article-title":"Fast and Robust Self-Representation Method for Hyperspectral Band Selection","volume":"10","author":"Sun","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/JSTARS.2013.2262926","article-title":"A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification","volume":"7","author":"Kuo","year":"2014","journal-title":"IEEE J. Select. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"42384","DOI":"10.1109\/ACCESS.2020.2977454","article-title":"Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks","volume":"8","author":"Tulczyjew","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cai, R., Yuan, Y., and Lu, X. (2018, January 23\u201326). Hyperspectral band selection with convolutional neural network. Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Guangzhou, China.","DOI":"10.1007\/978-3-030-03341-5_33"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1109\/TGRS.2019.2951433","article-title":"BS-Nets: An end-to-end framework for band selection of hyperspectral image","volume":"58","author":"Cai","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","first-page":"5501719","article-title":"Deep reinforcement learning for semisupervised hyperspectral band selection","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"084798","DOI":"10.1117\/1.JRS.8.084798","article-title":"Hyperspectral band selection based on parallelparticle swarm optimization and impurity function band prioritization schemes","volume":"8","author":"Chang","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1080\/15481603.2015.1075180","article-title":"Band selection in hyperspectral imagery using spatial cluster mean and genetic algorithms","volume":"52","author":"Paul","year":"2015","journal-title":"GISci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TNNLS.2015.2477537","article-title":"Salient band selection for hyperspectral image classification via manifold ranking","volume":"27","author":"Wang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/JSTARS.2011.2119466","article-title":"Fast Algorithms to Implement N-FINDR for Hyperspectral Endmember Extraction","volume":"4","author":"Xiong","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chang, C.-I. (2016). Real Time Progressive Hyperspectral Image Processing: Endmember Finding and Anomaly Detection, Springer.","DOI":"10.1007\/978-1-4419-6187-7"},{"key":"ref_40","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Springer Science & Business Media."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/TGRS.2003.819189","article-title":"Estimation of number of spectrally distinct signal sources in hyperspectral imagery","volume":"42","author":"Chang","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yu, H., Gao, L., Liao, W., and Zhang, B. (2018). Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification. Remote Sens., 18.","DOI":"10.3390\/s18061695"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sun, W., Jiang, M., Li, W., and Liu, Y. (2016). A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification. Remote Sens., 8.","DOI":"10.3390\/rs8030238"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/TGRS.2013.2240001","article-title":"Collaborative Sparse Regression for Hyperspectral Unmixing","volume":"52","author":"Iordache","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, C., Ma, Y., Mei, X., Liu, C., and Ma, J. (2016). Hyperspectral Unmixing with Robust Collaborative Sparse Regression. Remote Sens., 8.","DOI":"10.3390\/rs8070588"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Elhamifar, E., Sapiro, G., and Vidal, R. (2012, January 16\u201321). See all by looking at a few: Sparse modeling for finding representative objects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247852"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5028","DOI":"10.1109\/TGRS.2020.3011002","article-title":"A Fast Neighborhood Grouping Method for Hyperspectral Band Selection","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","unstructured":"Lozano, A.C., \u015awirszcz, G., and Abe, N. (2009, January 6\u201314). Group Orthogonal Matching Pursuit for variable selection and prediction. Proceedings of the 22nd International Conference on Neural Information Processing Systems, Red Hook, NY, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chang, C.-I. (2013). Hyperspectral Data Processing: Algorithm Design and Analysis, John Wiley & Sons.","DOI":"10.1002\/9781118269787"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"523","DOI":"10.14358\/PERS.79.6.523","article-title":"Band Grouping versus Band Clustering in SVM Ensemble Classification of Hyperspectral Imagery","volume":"79","author":"Bigdeli","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_51","unstructured":"(2022, October 06). Hyperspectral Remote Sensing Scenes. Available online: https:\/\/www.ehu.eus\/ccwintco\/index.php\/Hyperspectral_Remote_Sensing_Scenes."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN Feature Hierarchy for Hyperspectral Image Classification","volume":"17","author":"Roy","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5686\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:13:58Z","timestamp":1760145238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,10]]},"references-count":53,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225686"],"URL":"https:\/\/doi.org\/10.3390\/rs14225686","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,10]]}}}