{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:50:12Z","timestamp":1774630212130,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MEGASENSE research programme of the University of Helsinki, the City of Helsinki Innovation Fund, Business Finland, the European Commission through the Urban Innovative Action Healthy Outdoor Premises for Everyone (project No. UIA03-240)","award":["UIA03-240"],"award-info":[{"award-number":["UIA03-240"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated. Band selection is a technique that provides clear physical-meaning results for hyperspectral dimensional reduction, alleviating the difficulty for transferring and processing hyperspectral images caused by a property of hyperspectral images: large data volumes. In this study, a simple and efficient band ranking via extended coefficient of variation (BRECV) is proposed for unsupervised hyperspectral band selection. The naive idea of the BRECV algorithm is to select bands with relatively smaller means and lager standard deviations compared to their adjacent bands. To make this simple idea into an algorithm, and inspired by coefficient of variation (CV), we constructed an extended CV matrix for every three adjacent bands to study the changes of means and standard deviations, and accordingly propose a criterion to allocate values to each band for ranking. A derived unsupervised band selection based on the same idea while using entropy is also presented. Though the underlying idea is quite simple, and both cluster and optimization methods are not used, the BRECV method acquires qualitatively the same level of classification accuracy, compared with some state-of-the-art band selection methods<\/jats:p>","DOI":"10.3390\/rs12203319","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"3319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection"],"prefix":"10.3390","volume":"12","author":[{"given":"Peifeng","family":"Su","sequence":"first","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, FI-00014 Helsinki, Finland"},{"name":"Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sasu","family":"Tarkoma","sequence":"additional","affiliation":[{"name":"Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland"},{"name":"Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-9268","authenticated-orcid":false,"given":"Petri K. E.","family":"Pellikka","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, FI-00014 Helsinki, Finland"},{"name":"Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4913","DOI":"10.1109\/TGRS.2019.2894339","article-title":"A Novel Change Detection Method for Multitemporal Hyperspectral Images Based on Binary Hyperspectral Change Vectors","volume":"57","author":"Marinelli","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2018.09.018","article-title":"Invasive Tree Species Detection in the Eastern Arc Mountains Biodiversity Hotspot Using One Class Classification","volume":"218","author":"Piiroinen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s11554-017-0742-z","article-title":"A Real-Time Unsupervised Background Extraction-Based Target Detection Method for Hyperspectral Imagery","volume":"15","author":"Li","year":"2018","journal-title":"J. Real Time Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1109\/JSTARS.2018.2798661","article-title":"Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques","volume":"11","author":"Sellami","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MSP.2013.2279179","article-title":"Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods","volume":"31","author":"Tuia","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/JSTARS.2019.2899157","article-title":"Semi-Supervised Hyperspectral Band Selection Based on Dynamic Classifier Selection","volume":"12","author":"Cao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","first-page":"1","article-title":"Classification of Crops across Heterogeneous Agricultural Landscape in Kenya Using AisaEAGLE Imaging Spectroscopy Data","volume":"39","author":"Piiroinen","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"4360","DOI":"10.1109\/TGRS.2019.2890848","article-title":"Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection","volume":"57","author":"Wei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1109\/TGRS.2018.2868796","article-title":"Laplacian-Regularized Low-Rank Subspace Clustering for Hyperspectral Image Band Selection","volume":"57","author":"Zhai","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2596","DOI":"10.1109\/TGRS.2018.2875304","article-title":"Hyperspectral Image Denoising by Fusing the Selected Related Bands","volume":"57","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1049\/iet-ipr.2018.5423","article-title":"Band Selection of Hyperspectral Image by Sparse Manifold Clustering","volume":"13","author":"Das","year":"2019","journal-title":"IET Image Process."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Sumarsono, A., and Du, Q. (2014). Estimation of Number of Signal Subspaces in Hyperspectral Imagery Using Low-Rank Subspace Representation. Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, IEEE Computer Society.","DOI":"10.1109\/WHISPERS.2014.8077620"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/5326.661089","article-title":"Supervised Classification in High-Dimensional Space: Geometrical, Statistical, and Asymptotical Properties of Multivariate Data","volume":"28","author":"Jimenez","year":"1998","journal-title":"IEEE Trans. Syst. Man Cybern. Part. C Appl. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3185","DOI":"10.1109\/TGRS.2018.2794443","article-title":"Graph-Regularized Fast and Robust Principal Component Analysis for Hyperspectral Band Selection","volume":"56","author":"Sun","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, W., Li, X., and Zhao, L. (2019). Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection. Remote Sens., 11.","DOI":"10.3390\/rs11111341"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/TGRS.2015.2450759","article-title":"A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection","volume":"54","author":"Jia","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","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 Networks Learn. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep09915","article-title":"Joint Skewness and Its Application in Unsupervised Band Selection for Small Target Detection","volume":"5","author":"Geng","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7111","DOI":"10.1109\/TGRS.2014.2307880","article-title":"A Fast Volume-Gradient-Based Band Selection Method for Hyperspectral Image","volume":"52","author":"Geng","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1109\/JSTSP.2015.2405902","article-title":"Efficient Unsupervised Band Selection through Spectral Rhythms","volume":"9","author":"Guimaraes","year":"2015","journal-title":"IEEE J. Sel. Top. Signal. Process."},{"key":"ref_25","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_26","first-page":"5910","article-title":"Optimal Clustering Framework for Hyperspectral Band Selection","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1109\/LGRS.2017.2765339","article-title":"Hyperspectral Band Selection Based on Deep Convolutional Neural Network and Distance Density","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","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_31","unstructured":"Baumgardner, M.F., Biehl, L.L., and Landgrebe, D.A. (2015). 220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3. Purdue Univ. Res. Repos."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, F., and Li, X. (2020). Hyperspectral Band Selection via Optimal Neighborhood Reconstruction. IEEE Trans. Geosci. Remote Sens., 1\u201312.","DOI":"10.1109\/TGRS.2020.2987955"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:19:58Z","timestamp":1760177998000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":32,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12203319"],"URL":"https:\/\/doi.org\/10.3390\/rs12203319","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,12]]}}}