{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:16:22Z","timestamp":1768565782160,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,9]],"date-time":"2019-01-09T00:00:00Z","timestamp":1546992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61601416"],"award-info":[{"award-number":["61601416"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61711530239"],"award-info":[{"award-number":["61711530239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701417"],"award-info":[{"award-number":["41701417"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, China  University of Geosciences, Wuhan","award":["CUG170612"],"award-info":[{"award-number":["CUG170612"]}]},{"name":"Hong Kong Scholars Program","award":["XJ2017030"],"award-info":[{"award-number":["XJ2017030"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant analysis (LDA) is a popular dimensionality reduction method for HSI classification, which assumes all the samples obey the same distribution. However, different samples may have different contributions in the computation of scatter matrices. To address the problem of feature redundancy, a new supervised HSI classification method based on locally weighted discriminant analysis (LWDA) is presented. The proposed LWDA method constructs a weighted discriminant scatter matrix model and an optimal projection matrix model for each training sample, which is on the basis of discriminant information and spatial-spectral information. For each test sample, LWDA searches its nearest training sample with spatial information and then uses the corresponding projection matrix to project the test sample and all the training samples into a low-dimensional feature space. LWDA can effectively preserve the spatial-spectral local structures of the original HSI data and improve the discriminating power of the projected data for the final classification. Experimental results on two real-world HSI datasets show the effectiveness of the proposed LWDA method compared with some state-of-the-art algorithms. Especially when the data partition factor is small, i.e., 0.05, the overall accuracy obtained by LWDA increases by about     20 %     for Indian Pines and     17 %     for Kennedy Space Center (KSC) in comparison with the results obtained when directly using the original high-dimensional data.<\/jats:p>","DOI":"10.3390\/rs11020109","type":"journal-article","created":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T03:22:31Z","timestamp":1547090551000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Locally Weighted Discriminant Analysis for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"11","author":[{"given":"Xiaoyan","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Lefei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer, Wuhan University, Wuhan 430072, China"}]},{"given":"Jane","family":"You","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/JSTARS.2015.2406339","article-title":"Generation of Spectral-Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications","volume":"8","author":"Gevaert","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","unstructured":"Luo, F., Du, B., Zhang, L., Zhang, L., and Tao, D. (2018). Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image. IEEE Trans. Cybern., 1\u201314."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2018.2857804","article-title":"Hyperspectral Unmixing via Deep Convolutional Neural Networks","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","unstructured":"Liu, W., Ma, X., Zhou, Y., Tao, D., and Cheng, J. (2018). p-Laplacian Regularization for Scene Recognition. IEEE Trans. Cybern., 1\u201314."},{"key":"ref_5","unstructured":"Wang, Q., Liu, S., Chanussot, J., and Li, X. (2018). Scene Classification with Recurrent Attention of VHR Remote Sensing Images. IEEE Trans. Geosci. Remote Sens., 1\u201313."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7109","DOI":"10.1109\/TGRS.2018.2848473","article-title":"Scene Classification Based on Multiscale Convolutional Neural Network","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4581","DOI":"10.1109\/TGRS.2018.2828029","article-title":"SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery","volume":"56","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.inffus.2017.09.001","article-title":"Multiview dimension reduction via Hessian multiset canonical correlations","volume":"41","author":"Liu","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_10","unstructured":"Wang, Q., Qin, Z., Nie, F., and Li, X. (2018). Spectral Embedded Adaptive Neighbors Clustering. IEEE Trans. Neural Netw. Learn. Syst., 1\u20137."},{"key":"ref_11","unstructured":"Wang, Q., Chen, M., Nie, F., and Li, X. (2018). Detecting Coherent Groups in Crowd Scenes by Multiview Clustering. IEEE Trans. Pattern Anal. Mach. Intell., 1."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"4955","DOI":"10.1109\/TGRS.2013.2286195","article-title":"Hyperspectral Remote Sensing Image Subpixel Target Detection Based on Supervised Metric Learning","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/TIP.2015.2495116","article-title":"Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification","volume":"25","author":"Luo","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TCYB.2016.2605044","article-title":"Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images","volume":"48","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TCSVT.2007.906936","article-title":"Which Components Are Important For Interactive Image Searching?","volume":"18","author":"Tao","year":"2008","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sun, W., Tian, L., Xu, Y., Du, B., and Du, Q. (2018). A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10030417"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Luo, F., Huang, H., Duan, Y., Liu, J., and Liao, Y. (2017). Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9080790"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2874","DOI":"10.1109\/TIP.2015.2432713","article-title":"Single Image Superresolution via Directional Group Sparsity and Directional Features","volume":"24","author":"Li","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","unstructured":"Su, H., Zhao, B., Du, Q., and Du, P. (2018). Kernel Equation Collaborative Representation With Local Correlation Features for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens., 1\u201312."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","article-title":"Limitations of Principal Components Analysis for Hyperspectral Target Recognition","volume":"5","author":"Prasad","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TCYB.2013.2273355","article-title":"Fisher Discriminant Analysis With L1-Norm","volume":"44","author":"Wang","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Z., and He, B. (2011, January 24\u201326). Locality perserving projections algorithm for hyperspectral image dimensionality reduction. Proceedings of the 2011 19th International Conference on Geoinformatics, Shanghai, China.","DOI":"10.1109\/GeoInformatics.2011.5980790"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.neucom.2014.02.026","article-title":"Single image super-resolution via subspace projection and neighbor embedding","volume":"139","author":"Li","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, M., Wang, Q., and Li, X. (2018). Discriminant Analysis with Graph Learning for Hyperspectral Image Classification. Remote Sens., 10.","DOI":"10.3390\/rs10060836"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TGRS.2014.2333539","article-title":"Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification","volume":"53","author":"Zhou","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1109\/LGRS.2017.2751559","article-title":"Locality Adaptive Discriminant Analysis for Spectral-Spatial Classification of Hyperspectral Images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1109\/JSTARS.2014.2315786","article-title":"Collaborative Graph-Based Discriminant Analysis for Hyperspectral Imagery","volume":"7","author":"Ly","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, L., and You, J. (2018). Hyperspectral Image Classification Based on Two-stage Subspace Projection. Remote Sens., 10.","DOI":"10.3390\/rs10101565"},{"key":"ref_30","unstructured":"(2014, July 31). Hyperspectral Remote Sensing Scenes. Available online: http:\/\/www.ehu.eus\/ccwintco\/index.php?title=          Hyperspectral_Remote_Sensing_Scenes."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1109\/TCYB.2015.2493161","article-title":"Semi-Supervised SVM with Extended Hidden Features","volume":"46","author":"Dong","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_32","unstructured":"Eyden, R.J.V., Wit, P.W.C.D., and Arron, J.C. (1995, January 9\u201311). Predicting company failure-a comparison between neural networks and established statistical techniques by applying the McNemar test. Proceedings of the 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr), New York, NY, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1109\/TGRS.2011.2174156","article-title":"Latent Class Modeling for Site- and Non-Site-Specific Classification Accuracy Assessment Without Ground Data","volume":"50","author":"Foody","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","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. Intell. Syst. Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:24:39Z","timestamp":1760185479000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,9]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020109"],"URL":"https:\/\/doi.org\/10.3390\/rs11020109","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,9]]}}}