{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T21:37:08Z","timestamp":1772919428413,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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 this paper, a novel feature line embedding (FLE) algorithm based on support vector machine (SVM), referred to as SVMFLE, is proposed for dimension reduction (DR) and for improving the performance of the generative adversarial network (GAN) in hyperspectral image (HSI) classification. The GAN has successfully shown high discriminative capability in many applications. However, owing to the traditional linear-based principal component analysis (PCA) the pre-processing step in the GAN cannot effectively obtain nonlinear information; to overcome this problem, feature line embedding based on support vector machine (SVMFLE) was proposed. The proposed SVMFLE DR scheme is implemented through two stages. In the first scatter matrix calculation stage, FLE within-class scatter matrix, FLE between-scatter matrix, and support vector-based FLE between-class scatter matrix are obtained. Then in the second weight determination stage, the training sample dispersion indices versus the weight of SVM-based FLE between-class matrix are calculated to determine the best weight between-scatter matrices and obtain the final transformation matrix. Since the reduced feature space obtained by the SVMFLE scheme is much more representative and discriminative than that obtained using conventional schemes, the performance of the GAN in HSI classification is higher. The effectiveness of the proposed SVMFLE scheme with GAN or nearest neighbor (NN) classifiers was evaluated by comparing them with state-of-the-art methods and using three benchmark datasets. According to the experimental results, the performance of the proposed SVMFLE scheme with GAN or NN classifiers was higher than that of the state-of-the-art schemes in three performance indices. Accuracies of 96.3%, 89.2%, and 87.0% were obtained for the Salinas, Pavia University, and Indian Pines Site datasets, respectively. Similarly, this scheme with the NN classifier also achieves 89.8%, 86.0%, and 76.2% accuracy rates for these three datasets.<\/jats:p>","DOI":"10.3390\/rs13010130","type":"journal-article","created":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T22:35:48Z","timestamp":1609540548000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Feature Line Embedding Based on Support Vector Machine for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5448-9900","authenticated-orcid":false,"given":"Ying-Nong","family":"Chen","sequence":"first","affiliation":[{"name":"Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli Dist., Taoyuan City 32001, Taiwan"},{"name":"Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Rd., Jhongli Dist., Taoyuan City 32001, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2538-1108","authenticated-orcid":false,"given":"Tipajin","family":"Thaipisutikul","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Rd., Salaya, Nakhon Pathom 73170, Thailand"}]},{"given":"Chin-Chuan","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National United University, No. 1, Lienda, Miaoli 36003, Taiwan"}]},{"given":"Tzu-Jui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Rd., Jhongli Dist., Taoyuan City 32001, Taiwan"}]},{"given":"Kuo-Chin","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Rd., Jhongli Dist., Taoyuan City 32001, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, Y.S., Huang, L.B., Lin, Z., Yokoya, N.T., and Jia, X.P. (2019). Fine-grained classification of hyperspectral imagery based on deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11222690"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gualtieri, J.A., and Cromp, R.F. (1998, January 14\u201316). Support vector machines for hyperspectral remote sensing classification. Proceedings of the 27th AIPR Workshop: Advances in Computer-Assisted Recognition, Washington, DC, USA.","DOI":"10.1117\/12.339824"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"14292","DOI":"10.3390\/rs71114292","article-title":"A dimension reduction framework for HSI classification using fuzzy and kernel NFLE transformation","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/TGRS.2013.2238635","article-title":"Hyperspectral image classification using nearest feature line embedding approach","volume":"52","author":"Chang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1109\/TGRS.2011.2168532","article-title":"Laplacian eigenmaps-based polarimetric dimensionality reduction for SAR image classification","volume":"50","author":"Tu","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/TGRS.2011.2165957","article-title":"Locality-preserving dimensionality reduction and classification for hyperspectral image analysis","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative adversarial networks for hyperspectral image classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","unstructured":"Turk, M., and Pentland, A.P. (1991, January 3\u20136). Face recognition using Eigenfaces. Proceedings of the 1991 Proceedings CVPR \u201991. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Maui, HI, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/34.598228","article-title":"Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection","volume":"19","author":"Belhumeur","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TPAMI.2005.9","article-title":"Discriminative common vectors for face recognition","volume":"27","author":"Cevikalp","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2005.55","article-title":"Face recognition using Laplacianfaces","volume":"27","author":"He","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Z., and He, B. (2011, January 24\u201326). Locality preserving 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_16","unstructured":"Kim, D.H., and Finkel, L.H. (2003, January 20\u201322). Hyperspectral image processing using locally linear embedding. Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, Capri, Italy."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/LGRS.2011.2128854","article-title":"Locality-preserving discriminant analysis in kernel-induced feature spaces for hyperspectral image classification","volume":"8","author":"Li","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.11591\/telkomnika.v10i5.1346","article-title":"Discriminative supervised neighborhood preserving embedding feature extraction for hyperspectral-image classification","volume":"10","author":"Luo","year":"2012","journal-title":"Telkomnika"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1016\/j.patcog.2014.12.016","article-title":"Ensemble manifold regularized sparse low-rank approximation for multi-view feature embedding","volume":"48","author":"Zhang","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_20","unstructured":"Boots, B., and Gordon, G.J. (July, January 26). Two-manifold problems with applications to nonlinear system identification. Proceedings of the 29th International Conference on Machine Learning, Edinburgh, UK."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/TIP.2004.840701","article-title":"Building kernels from binary strings for image matching","volume":"14","author":"Odone","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1162\/089976698300017467","article-title":"Nonlinear component analysis as a kernel eigenvalue problem","volume":"10","author":"Scholkopf","year":"1998","journal-title":"Neural Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TPAMI.2010.183","article-title":"Multiple kernel learning for dimensionality reduction","volume":"33","author":"Lin","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1854","DOI":"10.1016\/j.patcog.2014.12.001","article-title":"Two-stage multiple kernel learning for supervised dimensionality reduction","volume":"48","author":"Nazarpour","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1109\/TGRS.2012.2230268","article-title":"Generalized composite kernel framework for hyperspectral image classification","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TGRS.2012.2201730","article-title":"Hyperspectral image classification via kernel sparse representation","volume":"51","author":"Chen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/TGRS.2017.2769673","article-title":"Supervised deep feature extraction for hyperspectral image classification","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1109\/TGRS.2018.2860464","article-title":"Feature extraction with multiscale covariance maps for hyperspectral image classification","volume":"57","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","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."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1109\/TGRS.2019.2946318","article-title":"Deep metric learning-based feature embedding for hyperspectral image classification","volume":"58","author":"Deng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1109\/TGRS.2018.2868851","article-title":"Active transfer learning network: A unified deep joint spectral-spatial feature learning model for hyperspectral image classification","volume":"57","author":"Deng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","first-page":"125","article-title":"Support vector clustering","volume":"2","author":"Horn","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TPAMI.2007.250598","article-title":"Graph embedding and extensions: A framework for dimensionality reduction","volume":"29","author":"Yan","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:06:16Z","timestamp":1760159176000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13010130"],"URL":"https:\/\/doi.org\/10.3390\/rs13010130","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,1]]}}}