{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T08:32:00Z","timestamp":1777537920050,"version":"3.51.4"},"reference-count":57,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,3,15]],"date-time":"2016-03-15T00:00:00Z","timestamp":1458000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"41401389","award":["National Natural Science Foundation"],"award-info":[{"award-number":["National Natural Science Foundation"]}]},{"name":"2015M570668","award":["the 57th Chinese Postdoctoral Science Foundation"],"award-info":[{"award-number":["the 57th Chinese Postdoctoral Science Foundation"]}]},{"name":"Ningbo Social Science and Technology Project","award":["2014C50067"],"award-info":[{"award-number":["2014C50067"]}]},{"DOI":"10.13039\/100007834","name":"Ningbo Natural Science Foundation","doi-asserted-by":"publisher","award":["2014A610173"],"award-info":[{"award-number":["2014A610173"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. The method formulates band selection into a famous problem of archetypal analysis and selects the representative bands by finding the archetypes in the minimal convex hull containing the HSI band points (i.e., one band corresponds to a band point in the high-dimensional feature space). Without any other parameter tuning work except the size of band subset, the SSR optimizes the band selection program using the block-coordinate descent scheme. Four state-of-the-art methods are utilized to make comparisons with the SSR on the Indian Pines and PaviaU HSI datasets. Experimental results illustrate that SSR outperforms all four methods in classification accuracies (i.e., Average Classification Accuracy (ACA) and Overall Classification Accuracy (OCA)) and three quantitative evaluation results (i.e., Average Information Entropy (AIE), Average Correlation Coefficient (ACC) and Average Relative Entropy (ARE)), whereas it takes the second shortest computational time. Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications.<\/jats:p>","DOI":"10.3390\/rs8030238","type":"journal-article","created":{"date-parts":[[2016,3,15]],"date-time":"2016-03-15T11:10:18Z","timestamp":1458040218000},"page":"238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification"],"prefix":"10.3390","volume":"8","author":[{"given":"Weiwei","family":"Sun","sequence":"first","affiliation":[{"name":"Faculty of Architectural Engineering, Civil Engineering and Environment, Ningbo University, Ningbo 315211, China"},{"name":"Qidong Photoelectric Remote Sensing Center, Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Qidong 226200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Jiang","sequence":"additional","affiliation":[{"name":"Faculty of Architectural Engineering, Civil Engineering and Environment, Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiyue","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinnian","family":"Liu","sequence":"additional","affiliation":[{"name":"Qidong Photoelectric Remote Sensing Center, Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Qidong 226200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.rse.2014.04.034","article-title":"Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake","volume":"157","author":"Giardino","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.rse.2015.05.003","article-title":"Remote monitoring of giant kelp biomass and physiological condition: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission","volume":"167","author":"Bell","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1127\/pfg\/2015\/0256","article-title":"Low-weight and uav-based hyperspectral full-frame cameras for monitoring crops: Spectral comparison with portable spectroradiometer measurements","volume":"1","author":"Bareth","year":"2015","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop lai using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"19","article-title":"Temperature and emissivity separation and mineral mapping based on airborne tasi hyperspectral thermal infrared data","volume":"40","author":"Cui","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","first-page":"152","article-title":"Using airborne hyperspectral data to characterize the surface ph and mineralogy of pyrite mine tailings","volume":"32","author":"Zabcic","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","unstructured":"Donoho, D.L. (2000, January 6\u201311). High-dimensional data analysis: The curses and blessings of dimensionality. Proceedings of the 2000 American Math Society Math Challenges of the 21st Century, Los Angeles, CA, USA."},{"key":"ref_8","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 Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1109\/TGRS.2007.895416","article-title":"Semi-supervised graph-based hyperspectral image classification","volume":"45","author":"Marsheva","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/TGRS.2012.2197860","article-title":"Tensor discriminative locality alignment for hyperspectral image spectral\u2013spatial feature extraction","volume":"51","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/TGRS.2010.2081677","article-title":"Random-selection-based anomaly detector for hyperspectral imagery","volume":"49","author":"Du","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","unstructured":"Tong, Q.X., Zhang, B., and Zheng, L.-F. (2006). Hyperspectral Remote Sensing: Principle, Technology and Application, Higher Education Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TGRS.2011.2162339","article-title":"On combining multiple features for hyperspectral remote sensing image classification","volume":"50","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/JSTARS.2013.2238890","article-title":"Nonlinear dimensionality reduction via the ENH-LTSA method for hyperspectral image classification","volume":"7","author":"Sun","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"793","DOI":"10.14358\/PERS.70.7.793","article-title":"Methodology for hyperspectral band selection","volume":"70","author":"Bajcsy","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Arzuaga-Cruz, E., Jimenez-Rodriguez, L.O., and Velez-Reyes, M. (2003). Unsupervised feature extraction and band subset selection techniques based on relative entropy criteria for hyperspectral data analysis. Proc. SPIE, 5093.","DOI":"10.1117\/12.485942"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TGRS.2004.830549","article-title":"Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries","volume":"42","author":"Keshava","year":"2004","journal-title":"IEEE Trans. Geosc. Remote Sens."},{"key":"ref_19","first-page":"55","article-title":"Optimum band selection for supervised classification of multispectral data","volume":"56","author":"Mausel","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1109\/LGRS.2006.878240","article-title":"Band selection for hyperspectral image classification using mutual information","volume":"3","author":"Guo","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"4800","DOI":"10.1109\/TGRS.2012.2230445","article-title":"Semisupervised discriminative locally enhanced alignment for hyperspectral image classification","volume":"51","author":"Shi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/JSTARS.2012.2185822","article-title":"Particle swarm optimization-based hyperspectral dimensionality reduction for urban land cover classification","volume":"5","author":"Yang","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chang, Y.L., Fang, J.P., Benediktsson, J.A., Chang, L.Y., Ren, H., and Chen, K.S. (2009, January 12\u201317). Band selection for hyperspectral images based on parallel particle swarm optimization schemes. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417728"},{"key":"ref_26","first-page":"325","article-title":"A band selection method for hyperspectral image classification based on improved particle swarm optimization","volume":"8","author":"Shen","year":"2015","journal-title":"Int. J. Signal Process. Image Process. Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.4028\/www.scientific.net\/AMM.675-677.1158","article-title":"Band selection of hyperspectral chlorophyll-a concentration inversion based on parallel ant colony algorithm","volume":"675","author":"Tang","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gao, J., Du, Q., Gao, L., Sun, X., and Zhang, B. (2014). Ant colony optimization-based supervised and unsupervised band selections for hyperspectral urban data classification. J. Appl. Remote Sens., 8.","DOI":"10.1117\/1.JRS.8.085094"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1109\/LGRS.2012.2236819","article-title":"Band selection for hyperspectral imagery: A new approach based on complex networks","volume":"10","author":"Xia","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xia, W., Dong, Z., Pu, H., Wang, B., and Zhang, L. (2012, January 22\u201327). Network topology analysis: A new method for band selection. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350779"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"4589","DOI":"10.1080\/2150704X.2014.930196","article-title":"A band selection approach for small target detection based on cem","volume":"35","author":"Sun","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Du, Q., Bioucas-Dias, J.M., and Plaza, A. (2012, January 22\u201327). Hyperspectral band selection using a collaborative sparse model. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350781"},{"key":"ref_35","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 in Image Processing (ICIP), Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6116223"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/MSP.2013.2279507","article-title":"Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection","volume":"31","author":"Willett","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_38","first-page":"1","article-title":"Introduction to compressed sensing","volume":"93","author":"Davenport","year":"2011","journal-title":"Electr. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s12145-014-0201-3","article-title":"Band selection using sparse nonnegative matrix factorization with the thresholded earth\u2019s mover distance for hyperspectral imagery classification","volume":"8","author":"Sun","year":"2015","journal-title":"Earth Sci. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1080\/2150704X.2013.870674","article-title":"Constrained nonnegative matrix factorization and hyperspectral image dimensionality reduction","volume":"5","author":"Xiao","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chepushtanova, S., Gittins, C., and Kirby, M. (2014). Band selection in hyperspectral imagery using sparse support vector machines. Proc. SPIE, 9088.","DOI":"10.1117\/12.2063812"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1080\/2150704X.2014.993482","article-title":"Band selection for target detection in hyperspectral imagery using sparse cem","volume":"5","author":"Geng","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/TGRS.2014.2321405","article-title":"Automatic spatial\u2013spectral feature selection for hyperspectral image via discriminative sparse multimodal learning","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/TGRS.2014.2326655","article-title":"Hyperspectral band selection by multitask sparsity pursuit","volume":"53","author":"Yuan","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/LGRS.2014.2337957","article-title":"A new sparsity-based band selection method for target detection of hyperspectral image","volume":"12","author":"Sun","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","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 Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"M\u00f8rup, M., and Hansen, L.K. (September, January 29). Archetypal analysis for machine learning. Proceedings of the 2010 IEEE International Workshop on the Machine Learning for Signal Processing (MLSP), Kittil\u00e4, Finland.","DOI":"10.1109\/MLSP.2010.5589222"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1080\/00401706.1994.10485840","article-title":"Archetypal analysis","volume":"36","author":"Cutler","year":"1994","journal-title":"Technometrics"},{"key":"ref_49","unstructured":"Bauckhage, C. A Note on Archetypal Analysis and the Approximation of Convex Hulls. Available online: http:\/\/arxiv.org\/pdf\/1410.0642v1.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, Y., Mairal, J., and Harchaoui, Z. (2014, January 24\u201327). Fast and robust archetypal analysis for representation learning. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.192"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2011.06.033","article-title":"Archetypal analysis for machine learning and data mining","volume":"80","author":"Hansen","year":"2012","journal-title":"Neurocomputing"},{"key":"ref_52","unstructured":"Nocedal, J., and Wright, S. (2006). Numerical Optimization, Springer Science & Business Media."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Steinwart, I., and Christmann, A. (2008). Support Vector Machines, Springer Verlag.","DOI":"10.1007\/978-0-387-77242-4"},{"key":"ref_54","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"},{"key":"ref_55","first-page":"18","article-title":"Classification and regression by random forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"27","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_57","unstructured":"Breiman, L. Randomforest: Breiman and Cutler\u2019s Random Forests for Classification and Regression, URL R Package Version. Available online: http:\/\/stat-www.berkeley.edu\/users\/breiman\/RandomForests."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/3\/238\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:20:44Z","timestamp":1760210444000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/3\/238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,15]]},"references-count":57,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,3]]}},"alternative-id":["rs8030238"],"URL":"https:\/\/doi.org\/10.3390\/rs8030238","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,3,15]]}}}