{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:50:42Z","timestamp":1761709842505,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/rs13071253","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T21:09:45Z","timestamp":1616706585000},"page":"1253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8234-168X","authenticated-orcid":false,"given":"Guichi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"}]},{"given":"Lei","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada"}]},{"given":"Lin","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., and Zhao, C. (2020). A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sens., 12.","DOI":"10.3390\/rs12193188"},{"key":"ref_2","first-page":"1","article-title":"Spatial functional data analysis for the spatial-spectral classification of hyperspectral imagery","volume":"16","author":"Lv","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10310","DOI":"10.1109\/TGRS.2019.2933555","article-title":"Wavelet-domain low-rank\/group-sparse destriping for hyperspectral imagery","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, H., Chen, M., and Duan, Y. (2019). Dimensionality reduction of hyperspectral image using spatial-spectral regularized sparse hypergraph embedding. Remote Sens., 11.","DOI":"10.3390\/rs11091039"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hennessy, A., Clarke, K., and Lewis, M. (2020). Hyperspectral classification of plants: A review of waveband selection generalisability. Remote Sens., 12.","DOI":"10.3390\/rs12010113"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MGRS.2018.2854840","article-title":"New frontiers in spectral-spatial hyperspectral image classification: The latest advances based on mathematical morphology, Markov random fields, segmentation, sparse representation, and deep learning","volume":"6","author":"Ghamisi","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep learning for classification of hyperspectral data: A comparative review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep learning for hyperspectral image classification: An overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust face recognition via space representation","volume":"31","author":"Wright","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","article-title":"Hyperspectral image classification using dictionary-based sparse representation","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1109\/JSTARS.2013.2264720","article-title":"A nonlocal weighted joint sparse representation classification method for hyperspectral imagery","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hu, S., Peng, J., Fu, Y., and Li, L. (2019). Kernel joint sparse representation based on self-paced learning for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11091114"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/LGRS.2016.2532380","article-title":"Hyperspectral image classification with robust sparse representation","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4142","DOI":"10.1109\/JSTARS.2016.2593907","article-title":"Spectral-spatial feature learning using cluster-based group sparse coding for hyperspectral image classification","volume":"9","author":"Zhang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","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. Sensors, 18.","DOI":"10.3390\/s18061695"},{"key":"ref_16","unstructured":"Zhang, L., Yang, M., and Feng, X. (2011, January 3\u20136). Sparse representation or collaborative representation: Which helps face recognition?. Proceedings of the 2011 International Conference on Computer Vision, Institute of Electrical and Electronics Engineers, Barcelona, Spain."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.neucom.2014.09.035","article-title":"Weighted sparse representation for face recognition","volume":"151","author":"Fan","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1109\/TGRS.2013.2241773","article-title":"Nearest regularized subspace for hyperspectral classification","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","first-page":"1","article-title":"Kernel collaborative representation with tikhonov regularization for hyperspectral image classification","volume":"12","author":"Li","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4664","DOI":"10.1109\/TGRS.2018.2833882","article-title":"Multikernel adaptive collaborative representation for hyperspectral image classification","volume":"56","author":"Du","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7246","DOI":"10.1109\/TGRS.2019.2912507","article-title":"Structure-aware collaborative representation for hyperspectral image classification","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tu, B., Zhou, C., Liao, X., Zhang, G., and Peng, Y. (2020). Spectral-spatial hyperspectral classification via structural-kernel collaborative representation. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2020.2988124"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1109\/TGRS.2010.2098413","article-title":"Sparse unmixing of hyperspectral data","volume":"49","author":"Iordache","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4178","DOI":"10.1109\/JSTARS.2016.2542113","article-title":"Hyperspectral image classification by fusing collaborative and sparse representations","volume":"9","author":"Li","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/LGRS.2017.2671852","article-title":"Kernel fused representation-based classifier for hyperspectral imagery","volume":"14","author":"Gan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1109\/LGRS.2019.2898913","article-title":"Hyperspectral image classification using kernel fused representation via a spatial-spectral composite kernel with ideal regularization","volume":"16","author":"Liu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bian, X., Chen, C., Xu, Y., and Du, Q. (2016). Robust hyperspectral image classification by multi-layer spatial-spectral sparse representations. Remote Sens., 8.","DOI":"10.3390\/rs8120985"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4102","DOI":"10.1109\/JSTARS.2016.2559524","article-title":"Bilayer elastic net regression model for supervised spectral-spatial hyperspectral image classification","volume":"9","author":"Soomro","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2922","DOI":"10.1109\/JSTARS.2017.2666118","article-title":"Local and nonlocal context-aware elastic net representation-based classification for hyperspectral images","volume":"10","author":"Soomro","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1109\/TIP.2017.2765820","article-title":"Discriminative multiple canonical correlation analysis for information fusion","volume":"27","author":"Gao","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/TMM.2018.2859590","article-title":"The labeled multiple canonical correlation analysis for information fusion","volume":"21","author":"Gao","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1109\/TGRS.2017.2781805","article-title":"Multifeature dictionary learning for collaborative representation classification of hyperspectral imagery","volume":"56","author":"Su","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/LGRS.2015.2402971","article-title":"Fast multifeature joint sparse representation for hyperspectral image classification","volume":"12","author":"Zhang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3174","DOI":"10.1109\/TGRS.2015.2513082","article-title":"Gabor cube selection based multitask joint sparse representation for hyperspectral image classification","volume":"54","author":"Jia","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1109\/TIM.2017.2664480","article-title":"Hyperspectral image classification via multiple-feature-based adaptive sparse representation","volume":"66","author":"Fang","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6625","DOI":"10.1109\/TGRS.2016.2587672","article-title":"Fast three-dimensional empirical mode decomposition of hyperspectral images for class-oriented multitask learning","volume":"54","author":"He","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, Z., Wang, Y., and Hu, J. (2018). Joint sparse and low-rank multitask learning with laplacian-like regularization for hyperspectral classification. Remote Sens., 10.","DOI":"10.3390\/rs10020322"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5343","DOI":"10.1109\/TGRS.2018.2814781","article-title":"Multiple feature kernel sparse representation classifier for hyperspectral imagery","volume":"56","author":"Gan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/JSTARS.2013.2295313","article-title":"Gabor-filtering-based nearest regularized subspace for hyperspectral image classification","volume":"7","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1109\/TGRS.2014.2334608","article-title":"Gabor feature-based collaborative representation for hyperspectral imagery classification","volume":"53","author":"Jia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4644","DOI":"10.1109\/JSTARS.2014.2328618","article-title":"Morphological profiles based on differently shaped structuring elements for classification of images with very high spatial resolution","volume":"7","author":"Lv","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local binary patterns and extreme learning machine for hyperspectral imagery classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1137\/090756855","article-title":"NESTA: A fast and accurate first-order method for sparse recovery","volume":"4","author":"Becker","year":"2011","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1137\/090777761","article-title":"Alternating direction algorithms for \u21131-problems in compressive sensing","volume":"33","author":"Yang","year":"2011","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_46","unstructured":"Grave, E., Obozinski, G.R., and Bach, F.R. (2011). Trace lasso: A trace norm regularization for correlated designs. Adv. Neural Inf. Process. Syst."},{"key":"ref_47","unstructured":"Lin, Z., Chen, M., and Ma, Y. (2009). The Augmented Lagrange Multiplier Method for Exact Recovery of a Corrupted Low-Rank Matrices, Department of Electrical and Computer Engineering UIUC. UIUC Tech. Rep. UILU-ENG-09-2215."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1109\/TIP.2014.2380155","article-title":"Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization","volume":"24","author":"Lu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/cpa.20303","article-title":"Iteratively reweighted least squares minimization for sparse recovery","volume":"63","author":"Daubechies","year":"2010","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_50","first-page":"3441","article-title":"Iterative reweighted algorithms for matrix rank minimization","volume":"13","author":"Mohan","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Argyriou, A., Evgeniou, T., and Pontil, M. (2007). Multi-task feature learning. Adv. Neural Inf. Process. Syst.","DOI":"10.2139\/ssrn.1031158"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000015","article-title":"Optimization with sparsity-inducing penalties","volume":"4","author":"Bach","year":"2011","journal-title":"Found. Trends. Mach. Learn."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10994-007-5040-8","article-title":"Convex multi-task feature learning","volume":"73","author":"Argyriou","year":"2008","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1109\/TSP.2011.2179539","article-title":"Kernel sparse representation-based classifier","volume":"60","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7738","DOI":"10.1109\/TGRS.2014.2318058","article-title":"Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation","volume":"52","author":"Fang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"163712","DOI":"10.1016\/j.ijleo.2019.163712","article-title":"Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification","volume":"206","author":"Ahmad","year":"2020","journal-title":"Optik"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4237","DOI":"10.1109\/TGRS.2019.2961947","article-title":"Spatial-spectral feature extraction via deep ConvLSTM neural networks for hyperspectral image classification","volume":"58","author":"Hu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:41:08Z","timestamp":1760161268000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":58,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071253"],"URL":"https:\/\/doi.org\/10.3390\/rs13071253","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,3,25]]}}}