{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:47Z","timestamp":1760242907928,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,5]],"date-time":"2016-11-05T00:00:00Z","timestamp":1478304000000},"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":["61271408"],"award-info":[{"award-number":["61271408"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hyperspectral images by using kernel methods is investigated. For a given hyperspectral image, the principle component analysis (PCA) transform is first performed. Then, the first principle component of the input image is segmented into non-overlapping homogeneous regions by using the entropy rate superpixel (ERS) algorithm. Next, the local spectral histogram model is applied to each homogeneous region to obtain the corresponding texture features. Because this step is performed within each homogenous region, instead of within a fixed-size image window, the obtained local texture features in the image are more accurate, which can effectively benefit the improvement of classification accuracy. In the following step, a contextual spectral-texture kernel is constructed by combining spectral information in the image and the extracted texture information using the linearity property of the kernel methods. Finally, the classification map is achieved by the support vector machines (SVM) classifier using the proposed spectral-texture kernel. Experiments on two benchmark airborne hyperspectral datasets demonstrate that our method can effectively improve classification accuracies, even though only a very limited training sample is available. Specifically, our method can achieve from 8.26% to 15.1% higher in terms of overall accuracy than the traditional SVM classifier. The performance of our method was further compared to several state-of-the-art classification methods of hyperspectral images using objective quantitative measures and a visual qualitative evaluation.<\/jats:p>","DOI":"10.3390\/rs8110919","type":"journal-article","created":{"date-parts":[[2016,11,7]],"date-time":"2016-11-07T10:36:50Z","timestamp":1478515010000},"page":"919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1347-7030","authenticated-orcid":false,"given":"Yi","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Haiwei","family":"Song","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_2","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 Peocess. Mag."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Landgrebe, D.A. (2003). Signal Theory Methods in Multispectral Remote Sensing, Wiley-Interscience.","DOI":"10.1002\/0471723800"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1016\/j.patcog.2008.04.013","article-title":"Statistical pattern recognition in remote sensing","volume":"41","author":"Chen","year":"2008","journal-title":"Pattern Recognit."},{"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":"2271","DOI":"10.1109\/TGRS.2009.2037898","article-title":"Semisupervised neural networks for efficient hyperspectral image classification","volume":"48","author":"Ratle","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1080\/014311699213622","article-title":"A back-propagation neural network for mineralogical mapping from AVIRIS data","volume":"20","author":"Yang","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","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_10","unstructured":"Bennett, K.P., and Demiriz, A. (1999). Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II, MIT Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/TGRS.2011.2159726","article-title":"SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images","volume":"50","author":"Moustakidis","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Camps-Valls, G., and Bruzzone, L. (2009). Kernel Methods for Remote Sensing Data Analysis, Wiley.","DOI":"10.1002\/9780470748992"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1109\/TGRS.2007.895416","article-title":"Semi-supervised graph-based hyperspectral image classification","volume":"45","author":"Marsheva","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in spectral-spatial classification of hyperspectral images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0924-2716(98)00027-6","article-title":"Optimisation of building detection in satellite images by combining multispectral classification and texture filtering","volume":"54","author":"Zhang","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1080\/01431160802559046","article-title":"A comparative study of spatial approaches for urban mapping using hyperspectral rosis images over Pavia City, northern Italy","volume":"30","author":"Huang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1109\/LGRS.2010.2091253","article-title":"Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis","volume":"8","author":"Mura","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/LGRS.2012.2191761","article-title":"Discriminative Gabor feature selection for hyperspectral image classification","volume":"10","author":"Shen","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5795","DOI":"10.3390\/rs6065795","article-title":"Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine","volume":"6","author":"Chen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TGRS.2011.2162649","article-title":"Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.1109\/TGRS.2014.2361618","article-title":"Spectral-spatial classification for hyperspectral data using rotation forests with local feature extraction and Markov random fields","volume":"53","author":"Xia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/LGRS.2005.857031","article-title":"Composite kernels for hyperspectral image classification","volume":"3","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.patcog.2011.03.035","article-title":"A spatial\u2013spectral kernel-based approach for the classification of remote-sensing images","volume":"45","author":"Mathieu","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/LGRS.2010.2046618","article-title":"Spatio-spectral remote sensing image classification with graph kernels","volume":"7","author":"Shervashidze","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","article-title":"Segmentation and classification of hyperspectral images using watershed transformation","volume":"43","author":"Tarabalka","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1109\/TGRS.2008.2002577","article-title":"An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery","volume":"46","author":"Huang","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1109\/LGRS.2013.2257675","article-title":"Integration of segmentation techniques for classification of hyperspectral images","volume":"11","author":"Ghamisi","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/JSTARS.2011.2173466","article-title":"A marker-based approach for the automated selection of a single segmentation from a hierarchical set of image segmentations","volume":"5","author":"Tarabalka","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"296","DOI":"10.3390\/rs8040296","article-title":"A spectral-spatial hyperspectral image classification based on algebraic multigrid methods and hierarchical segmentation","volume":"8","author":"Song","year":"2016","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4186","DOI":"10.1109\/TGRS.2015.2392755","article-title":"Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/TGE.1976.294460","article-title":"Classification of multispectral image data by extraction and classification of homogeneous objects","volume":"14","author":"Kettig","year":"1976","journal-title":"IEEE Trans. Geosci. Electron."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/TSMCB.2009.2037132","article-title":"Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers","volume":"40","author":"Tarabalka","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tarabalka, Y., and Rana, A. (2014, January 13\u201318). Graph-cut-based model for spectral-spatial classification of hyperspectral images. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947216"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"748","DOI":"10.3390\/rs8090748","article-title":"Spectral-spatial classification of hyperspectral images using joint bilateral filter and graph cut based model","volume":"8","author":"Wang","year":"2016","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.patrec.2011.12.003","article-title":"Image segmentation using local spectral histograms and linear regression","volume":"33","author":"Yuan","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, M.Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1109\/TIP.2006.877511","article-title":"Image and texture segmentation using local spectral histograms","volume":"15","author":"Liu","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TGRS.2012.2234755","article-title":"Remote sensing image segmentation by combining spectral and texture features","volume":"52","author":"Yuan","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","unstructured":"Sch\u00f6lkopf, B., and Smola, A.J. (2002). Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1016\/S0042-6989(02)00297-3","article-title":"A spectral histogram model for texton modeling and texture discrimination","volume":"42","author":"Liu","year":"2002","journal-title":"Vis. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-based methods for hyperspectral image classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.patcog.2011.03.035","article-title":"A spatial-spectral kernel-based approach for the classification of remote-sensing images","volume":"45","author":"Fauvel","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TGRS.2013.2264508","article-title":"Spectral-spatial hyperspectral image classification with edge-preserving filtering","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2012.2205263","article-title":"Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bioucas-Dias, J.M., and Figueiredo, M. (2009). Logistic Regression via Variable Splitting and Augmented Lagrangian Tools, Instituto Superior Tecnico.","DOI":"10.1109\/WHISPERS.2009.5289072"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1109\/TGRS.2011.2128330","article-title":"Hyperspectral image segmentation using a new Bayesian approach with active learning","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chang, C.-C., and Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2.","DOI":"10.1145\/1961189.1961199"},{"key":"ref_49","first-page":"975","article-title":"Probability estimates for multiclass classification by pairwise coupling","volume":"5","author":"Wu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","unstructured":"Tadjudin, S., and Landgrebe, D. (1998). Classification of High Dimensional Data with Limited Training Samples, Purdue University."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/TIP.2007.894266","article-title":"Comparative study of semi-implicit schemes for nonlinear diffusion in hyperspectral imagery","volume":"16","author":"Castillo","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_52","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 University Research Repository."},{"key":"ref_53","unstructured":"Holzwarth, S., M\u00fcller, A., Habermeyer, M., Richter, R., Hausold, A., Thiemann, S., and Strobl, P. (2003, January 13\u201316). Hysens-DAIS\/ROSIS imaging spectrometers at DLR. Proceedings of the 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, Germany."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/11\/919\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:34:50Z","timestamp":1760211290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/11\/919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,5]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2016,11]]}},"alternative-id":["rs8110919"],"URL":"https:\/\/doi.org\/10.3390\/rs8110919","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,11,5]]}}}