{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:06:21Z","timestamp":1761491181935,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"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":["61420106007"],"award-info":[{"award-number":["61420106007"]}],"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>Due to its excellent performance in high-dimensional space, the kernel extreme learning machine has been widely used in pattern recognition and machine learning fields. In this paper, we propose a dual-weighted kernel extreme learning machine for hyperspectral imagery classification. First, diverse spatial features are extracted by guided filtering. Then, the spatial features and spectral features are composited by a weighted kernel summation form. Finally, the weighted extreme learning machine is employed for the hyperspectral imagery classification task. This dual-weighted framework guarantees that the subtle spatial features are extracted, while the importance of minority samples is emphasized. Experiments carried on three public data sets demonstrate that the proposed dual-weighted kernel extreme learning machine (DW-KELM) performs better than other kernel methods, in terms of accuracy of classification, and can achieve satisfactory results.<\/jats:p>","DOI":"10.3390\/rs13030508","type":"journal-article","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T11:40:48Z","timestamp":1612179648000},"page":"508","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Dual-Weighted Kernel Extreme Learning Machine for Hyperspectral Imagery Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2527-6444","authenticated-orcid":false,"given":"Xumin","family":"Yu","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanlong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingbiao","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Shaoguan University, Shaoguan 512023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8018-596X","authenticated-orcid":false,"given":"Shaohui","family":"Mei","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,1]]},"reference":[{"key":"ref_1","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 Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1117\/12.339824","article-title":"Support vector machines for hyperspectral remote sensing classification","volume":"3584","author":"Gualtieri","year":"1998","journal-title":"Proc. SPIE Workshop Adv. Comput. Assist. Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/LGRS.2005.857031","article-title":"Composite kernels for hyperspectral image classification","volume":"3","author":"Campsvalls","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","unstructured":"Fauvel, M., Chanussot, J., and Benediktsson, J.A. (2006, January 14\u201319). Evaluation of kernels for multiclass classification of hyperspectral remote sensing data. Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/JSTARS.2012.2227464","article-title":"Detection of Hedges in a Rural Landscape Using a Local Orientation Feature: From Linear Opening to Path Opening","volume":"6","author":"Fauvel","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.patrec.2009.12.018","article-title":"Advanced directional mathematical morphology of the detection of the road network in very high resolution remote sensing images","volume":"31","author":"Valero","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of Hyperspectral Images by Exploiting Spectral-Spatial Information of Super pixel via Multiple Kernels","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1109\/TIP.2017.2772836","article-title":"Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification","volume":"27","author":"Wu","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/LGRS.2019.2960528","article-title":"DSSNet: A Simple Dilated Semantic Segmentation Network for Hyperspectral Imagery Classification","volume":"17","author":"Pan","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1109\/JSTARS.2017.2655516","article-title":"R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method","volume":"10","author":"Pan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6808","DOI":"10.1109\/TGRS.2019.2908756","article-title":"Unsupervised Spatial\u2013Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification","volume":"57","author":"Mei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/TGRS.2018.2865102","article-title":"Self-Paced Joint Sparse Representation for the Classification of Hyperspectral Images","volume":"57","author":"Peng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TNNLS.2018.2874432","article-title":"Maximum Likelihood Estimation-Based Joint Sparse Representation for the Classification of Hyperspectral Remote Sensing Images","volume":"30","author":"Peng","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.1109\/TCYB.2015.2484324","article-title":"Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization","volume":"46","author":"Yuan","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1109\/TGRS.2015.2514161","article-title":"Nonlinear Multiple Kernel Learning with Multiple-Structure-Element Extended Morphological Profiles for Hyperspectral Image Classification","volume":"54","author":"Gu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"3399","DOI":"10.1109\/TGRS.2013.2272760","article-title":"Decision Fusion in Kernel-Induced Spaces for Hyperspectral Image Classification","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1109\/TGRS.2017.2768479","article-title":"Extinction Profiles Fusion for Hyperspectral Images Classification","volume":"56","author":"Fang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme Learning Machine for Regression and Multiclass Classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/2150704X.2013.805279","article-title":"Kernel-based extreme learning machine for remote sensing image classification","volume":"4","author":"Pal","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/JSTARS.2014.2359965","article-title":"Extreme Learning Machine with Composite Kernels for Hyperspectral Image Classification","volume":"8","author":"Zhou","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1080\/01431161.2014.999882","article-title":"ELM-based spectral\u2013spatial classification of hyperspectral images using extended morphological profiles and composite feature mappings","volume":"36","author":"Heras","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"22645","DOI":"10.1109\/ACCESS.2018.2825978","article-title":"Extreme Learning Machine with Enhanced Composite Feature for Spectral-Spatial Hyperspectral Image Classification","volume":"6","author":"Jiang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6263","DOI":"10.1109\/TGRS.2018.2828601","article-title":"Sparse Representation-Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral\u2013Spatial Classification of Hyperspectral Images","volume":"56","author":"Cao","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, J., Xi, B., Du, Q., Song, R., Li, Y., and Ren, G. (2018). Deep Kernel Extreme-Learning Machine for the Spectral\u2013Spatial Classification of Hyperspectral Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10122036"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.neucom.2019.01.010","article-title":"HCKBoost: Hybridized composite kernel boosting with extreme learning machines for hyperspectral image classification","volume":"334","author":"Ergul","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103013","DOI":"10.1016\/j.infrared.2019.103013","article-title":"Hyperspectral image classification based on average spectral-spatial features and improved hierarchical-ELM","volume":"102","author":"Le","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1007\/s13042-019-00926-5","article-title":"Hyperspectral image classification based on multiple reduced kernel extreme learning machine","volume":"10","author":"Lv","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/LGRS.2012.2203784","article-title":"Automatic Generation of Standard Deviation Attribute Profiles for Spectral\u2013Spatial Classification of Remote Sensing Data","volume":"10","author":"Marpu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/JSTARS.2016.2591004","article-title":"Firefly-Algorithm-Inspired Framework with Band Selection and Extreme Learning Machine for Hyperspectral Image Classification","volume":"10","author":"Su","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s11707-016-0603-2","article-title":"Optimized extreme learning machine for urban land cover classification using hyperspectral imagery","volume":"11","author":"Su","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"97296","DOI":"10.1117\/1.JRS.9.097296","article-title":"Optimizing extreme learning machine for hyperspectral image classification","volume":"9","author":"Li","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9021","DOI":"10.1109\/ACCESS.2017.2706363","article-title":"Remote Sensing Image Classification Based on Ensemble Extreme Learning Machine with Stacked Autoencoder","volume":"5","author":"Lv","year":"2017","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided Image Filtering","volume":"35","author":"He","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4177","DOI":"10.1109\/TGRS.2017.2689805","article-title":"Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images","volume":"55","author":"Pan","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s13638-019-1346-z","article-title":"Hyperspectral image classification with SVM and guided filter","volume":"2019","author":"Guo","year":"2019","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1016\/j.neucom.2017.10.046","article-title":"Discriminatively guided filtering (DGF) for hyperspectral image classification","volume":"275","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/TIP.2014.2371234","article-title":"Weighted Guided Image Filtering","volume":"24","author":"Li","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","unstructured":"Fletcher, R. (1981). Practical Methods of Optimization: Constrained Optimization, Wiley."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.neucom.2012.08.010","article-title":"Weighted extreme learning machine for imbalance learning","volume":"101","author":"Zong","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.neucom.2018.10.056","article-title":"Class imbalance learning using UnderBagging based kernelized extreme learning machine","volume":"329","author":"Raghuwanshi","year":"2019","journal-title":"Neurocomputing"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/508\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:18:09Z","timestamp":1760159889000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/508"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,1]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030508"],"URL":"https:\/\/doi.org\/10.3390\/rs13030508","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,2,1]]}}}