{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:20:47Z","timestamp":1772554847941,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,29]],"date-time":"2017-09-29T00:00:00Z","timestamp":1506643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural SciencFoundation of China","award":["41601354"],"award-info":[{"award-number":["41601354"]}]},{"name":"Natural SciencFoundation of China","award":["41601440"],"award-info":[{"award-number":["41601440"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD performance by selecting the most informative band subset from the original high-dimensional data space. In particular, for cases where ground reference data are available or unavailable, either supervised or unsupervised CD approaches are designed. The following sub-problems in HSI-CD are investigated, including: (1) the estimated number of multi-class changes; (2) the binary CD; (3) the multiple CD; (4) the estimated optimal number of selected bands; and (5) computational efficiency. The main contribution of this paper is to provide for the first time a thorough analysis of the impacts of band selection on the HSI-CD problem, thus to fix the gap in the state-of-the-art techniques either by simply utilizing the full dimensionality of the data or exploring a complex hierarchical change analysis. It is applicable to CD problems in multispectral or PolSAR images when the feature space is expanded for discriminant feature extraction. Two real multi-temporal hyperspectral Hyperion datasets are used to validate the proposed approaches. Quantitative and qualitative experimental results demonstrated that by selecting a subset of the most informative and distinct spectral bands, the proposed approaches offered better CD performance than the state-of-the-art techniques using original full bands, without losing the change representative and discriminable capabilities of a detector.<\/jats:p>","DOI":"10.3390\/rs9101008","type":"journal-article","created":{"date-parts":[[2017,9,29]],"date-time":"2017-09-29T12:24:04Z","timestamp":1506687844000},"page":"1008","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1612-4844","authenticated-orcid":false,"given":"Sicong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China"}]},{"given":"Qian","family":"Du","sequence":"additional","affiliation":[{"name":"College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China"},{"name":"Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA"}]},{"given":"Xiaohua","family":"Tong","sequence":"additional","affiliation":[{"name":"College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9091-6033","authenticated-orcid":false,"given":"Alim","family":"Samat","sequence":"additional","affiliation":[{"name":"Xinjiang Institute of Ecology and Geography, CAS and the CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China"}]},{"given":"Haiyan","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5616-9767","authenticated-orcid":false,"given":"Xiaolong","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/TGRS.2014.2321277","article-title":"Hierarchical change detection in multitemporal hyperspectral images","volume":"53","author":"Liu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.isprsjprs.2015.04.015","article-title":"Dimensionality reduction of hyperspectral images based on sparse discriminant manifold embedding","volume":"106","author":"Huang","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Change, C.-I. (2013). Hyperspectral Data Processing: Algorithm Design and Analysis, John Wiley & Sons, Inc.","DOI":"10.1002\/9781118269787"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1109\/JSTARS.2014.2301775","article-title":"E2LMs: Ensemble extreme learning machines for hyperspectral image classification","volume":"7","author":"Samat","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.isprsjprs.2014.04.006","article-title":"Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing","volume":"93","author":"Zabalza","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Du, P., Liu, S., Bruzzone, L., and Bovolo, F. (2012, January 22\u201327). Target-driven change detection based on data transformation and similarity measures. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350981"},{"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":"Wang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1109\/36.298007","article-title":"Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach","volume":"32","author":"Harsanyi","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/LGRS.2010.2053516","article-title":"An efficient method for supervised hyperspectral band selection","volume":"8","author":"Yang","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1109\/LGRS.2012.2233711","article-title":"A feature-metric-based affinity propagation technique for feature selection in hyperspectralimage classification","volume":"10","author":"Yang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5495","DOI":"10.1109\/TGRS.2015.2424236","article-title":"Hyperspectral band selection based on rough set","volume":"53","author":"Swarnajyoti","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","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_15","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":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.cviu.2013.03.007","article-title":"Multiview Hessian discriminative sparse coding for image annotation","volume":"118","author":"Liu","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_18","first-page":"5120","article-title":"p-Laplacian regularized sparse coding for human activity recognition","volume":"63","author":"Liu","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/JPROC.2012.2197169","article-title":"A novel framework for the design of change-detection systems for very-high-resolution remote sensing images","volume":"101","author":"Bruzzone","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TGRS.2006.885408","article-title":"A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain","volume":"45","author":"Bovolo","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2011.2171493","article-title":"A framework for automatic and unsupervised detection of multiple changes in multitemporal images","volume":"50","author":"Bovolo","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/JSTARS.2012.2200879","article-title":"Fusion of difference images for change detection over urban areas","volume":"5","author":"Du","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2012.05.006","article-title":"An automated approach for updating land cover maps based on integrated change detection and classification methods","volume":"71","author":"Chen","year":"2012","journal-title":"ISPRS J. Photogramm."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Q., Liu, L., and Wang, Y. (2017). Unsupervised change detection for multispectral remote sensing images using random walks. Remote Sens., 9.","DOI":"10.3390\/rs9050438"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2013.07.009","article-title":"A spectral gradient difference based approach for land cover change detection","volume":"85","author":"Chen","year":"2013","journal-title":"ISPRS J. Photogramm."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tang, Y., and Zhang, L. (2017). Urban change analysis with multi-sensor multispectral imagery. Remote Sens., 9.","DOI":"10.3390\/rs9030252"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.isprsjprs.2016.07.003","article-title":"Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition","volume":"119","author":"Xiao","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bruzzone, L., Liu, S., Bovolo, F., and Du, P. (2017). Change detection in multitemporal hyperspectral images. Multitemporal Remote Sensing: Methods and Applications, Springer.","DOI":"10.1007\/978-3-319-47037-5_4"},{"key":"ref_30","unstructured":"Schaum, A., and Stocker, A. (1998, January 10\u201315). Long-interval chronochrome target detection. Proceedings of the 1997 International Symposium on Spectral Sensing Research (ISSSR), San Diego, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1117\/12.544026","article-title":"Hyperspectral change detection and supervised matched filtering based on covariance equalization","volume":"5425","author":"Schaum","year":"2004","journal-title":"Proc. SPIE"},{"key":"ref_32","unstructured":"Frank, M., and Canty, M. (2003, January 27). Unsupervised change detection for hyperspectral images. Proceedings of the 12th JPL Airborne Earth Science Workshop, Pasadena, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"623312","DOI":"10.1117\/12.667961","article-title":"Change detection in hyperspectral imagery using temporal principal components","volume":"6233","author":"Roysam","year":"2006","journal-title":"Proc. SPIE"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, S., Bruzzone, L., Bovolo, F., and Du, P. (2012, January 4\u20137). Unsupervised hierarchical spectral analysis for change detection in hyperspectral images. Proceedings of the 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), Shanghai, China.","DOI":"10.1109\/WHISPERS.2012.6874245"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Du, Q., Younan, N., and King, R. (2007, January 18\u201320). Change analysis for hyperspectral imagery. Proceedings of the International Workshop on Analysis of Multi-temporal Remote Sensing Image, Leuven, Belgium.","DOI":"10.1109\/MULTITEMP.2007.4293052"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/JSTARS.2013.2241396","article-title":"A subspace-based change detection method for hyperspectral images","volume":"6","author":"Wu","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1109\/TGRS.2015.2396686","article-title":"Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images","volume":"53","author":"Liu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1109\/TGRS.2015.2505183","article-title":"Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images","volume":"54","author":"Liu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","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. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/36.843009","article-title":"Automatic analysis of the difference image for unsupervised change detection","volume":"38","author":"Bruzzone","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/LGRS.2016.2639540","article-title":"Oil spill detection via multitemporal optical remote sensing images: A change detection perspective","volume":"14","author":"Liu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","first-page":"262","article-title":"Land cover change detection over mining areas based on support vector machine","volume":"41","author":"Du","year":"2012","journal-title":"J. China Univ. Min. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"27:1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intel. Syst. Technol."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/LGRS.2012.2189934","article-title":"Empirical automatic estimation of the number of endmembers in hyperspectral images","volume":"10","author":"Luo","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2011). Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222-41"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/10\/1008\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:46:18Z","timestamp":1760208378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/10\/1008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,29]]},"references-count":47,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["rs9101008"],"URL":"https:\/\/doi.org\/10.3390\/rs9101008","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,29]]}}}