{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T05:58:50Z","timestamp":1772690330251,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,12]],"date-time":"2017-03-12T00:00:00Z","timestamp":1489276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61661136003"],"award-info":[{"award-number":["61661136003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471285"],"award-info":[{"award-number":["41471285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471351"],"award-info":[{"award-number":["41471351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371349"],"award-info":[{"award-number":["41371349"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601346"],"award-info":[{"award-number":["41601346"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2016YFD0300602"],"award-info":[{"award-number":["2016YFD0300602"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Funds for Technology innovation capacity building sponsored by the Beijing Academy of Agriculture and Forestry Sciences","award":["KJCX20170423"],"award-info":[{"award-number":["KJCX20170423"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral features or spatial features. The comparative experiments demonstrate the effectiveness of the proposed scheme.<\/jats:p>","DOI":"10.3390\/rs9030261","type":"journal-article","created":{"date-parts":[[2017,3,13]],"date-time":"2017-03-13T10:26:18Z","timestamp":1489400778000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images"],"prefix":"10.3390","volume":"9","author":[{"given":"Yuanyuan","family":"Fu","sequence":"first","affiliation":[{"name":"Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center of Agriculture Internet of Things, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1448-5091","authenticated-orcid":false,"given":"Chunjiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center of Agriculture Internet of Things, Beijing 100097, China"}]},{"given":"Jihua","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9916-6382","authenticated-orcid":false,"given":"Xiuping","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Engineering and Information Technology, University of New South Wales at Canberra, Canberra 2600, ACT, Australia"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center of Agriculture Internet of Things, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0294-5705","authenticated-orcid":false,"given":"Xiaoyu","family":"Song","sequence":"additional","affiliation":[{"name":"Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center of Agriculture Internet of Things, Beijing 100097, China"}]},{"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center of Agriculture Internet of Things, Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Jia, X. (2006). Remote Sensing Digital Image Analysis, Springer. [4th ed.].","DOI":"10.1007\/3-540-29711-1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0034-4257(98)00049-2","article-title":"Hyperspectral imaging and stress mapping in agriculture: A case study on wheat in Beauce (France)","volume":"66","author":"Lelong","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.rse.2004.03.013","article-title":"Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications","volume":"91","author":"Thenkabail","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s11119-007-9038-9","article-title":"Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging","volume":"8","author":"Huang","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.biosystemseng.2008.05.017","article-title":"Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes","volume":"101","author":"Mahesh","year":"2008","journal-title":"Biosyst. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4308","DOI":"10.3390\/s8074308","article-title":"A fixed-threshold approach to generate high-resolution vegetation maps for IKONOS imagery","volume":"8","author":"Cheng","year":"2008","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/LGRS.2009.2012442","article-title":"A new vegetation enhancement\/extraction technique for IKONOS and QuickBird imagery","volume":"6","author":"Tu","year":"2009","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/TGRS.2004.841417","article-title":"Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations","volume":"43","author":"Plaza","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1109\/LGRS.2005.848511","article-title":"A band selection technique for spectral classification","volume":"2","author":"Kempeneers","year":"2005","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/JPROC.2012.2229082","article-title":"Feature mining for hyperspectral image classification","volume":"101","author":"Jia","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_11","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_12","first-page":"214","article-title":"Multinomial logistic regression-based feature selection for hyperspectral data","volume":"14","author":"Pal","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/LGRS.2013.2264471","article-title":"Subspace detection using a mutual information measure for hyperspectral image classification","volume":"11","author":"Hossain","year":"2014","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","first-page":"27","article-title":"Conditional likelihood maximisation: A unifying framework for information theoretic feature selection","volume":"13","author":"Brown","year":"2012","journal-title":"J. Mach. Learn Res."},{"key":"ref_16","first-page":"1531","article-title":"Fast binary feature selection with conditional mutual information","volume":"5","author":"Fleuret","year":"2004","journal-title":"J. Mach. Learn Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/JSTSP.2008.923858","article-title":"Information-theoretic feature selection in microarray data using variable complementarity","volume":"2","author":"Meyer","year":"2008","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_18","unstructured":"Fukunaga, K. (2013). Introduction to Statistical Pattern Recognition, Academic Press."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1109\/TPAMI.2007.70799","article-title":"Feature selection with kernel class separability","volume":"30","author":"Wang","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1109\/TNN.2010.2044189","article-title":"Feature selection with redundancy-constrained class separability","volume":"21","author":"Zhou","year":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/0167-8655(94)90127-9","article-title":"Floating search methods in feature selection","volume":"15","author":"Pudil","year":"1994","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2004.843193","article-title":"Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data","volume":"43","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"4047","article-title":"Decision-level fusion of spectral reflectance and derivative information for robust hyperspectral land cover classification","volume":"48","author":"Kalluri","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","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_25","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":"Fauvel","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/29.1644","article-title":"Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression","volume":"36","author":"Daugman","year":"1988","journal-title":"IEEE Trans. Acoust. Speech. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/36.934070","article-title":"Best-bases feature extraction algorithms for classification of hyperspectral data","volume":"39","author":"Kumar","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1080\/01431161.2010.486416","article-title":"Using class-based feature selection for the classification of hyperspectral data","volume":"32","author":"Maghsoudi","year":"2011","journal-title":"INT. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.1109\/JSTARS.2015.2424433","article-title":"Semisupervised Pair-Wise Band Selection for Hyperspectral Images","volume":"8","author":"Bai","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_31","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_32","unstructured":"D. A. Landgrebe. Available online: https:\/\/engineering.purdue.edu\/~biehl\/MultiSpec\/hyperspectral.html."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1109\/TGRS.2009.2016214","article-title":"Spectral\u2013spatial classification of hyperspectral imagery based on partitional clustering techniques","volume":"47","author":"Tarabalka","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"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."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8424","DOI":"10.3390\/rs6098424","article-title":"A multichannel gray level co-occurrence matrix for multi\/hyperspectral image texture representation","volume":"6","author":"Huang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/TGRS.2004.825578","article-title":"Nonparametric weighted feature extraction for classification","volume":"42","author":"Kuo","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/3\/261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:30:17Z","timestamp":1760207417000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/3\/261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,12]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["rs9030261"],"URL":"https:\/\/doi.org\/10.3390\/rs9030261","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,12]]}}}