{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T03:35:03Z","timestamp":1767065703561,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,9,11]],"date-time":"2016-09-11T00:00:00Z","timestamp":1473552000000},"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>Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions obtained by the spectral-spatial segmentation process is presented. Our method includes the following steps. First, the probabilistic support vector machines (SVM) classifier is used to estimate probabilities belonging to each information class. Second, an extended JBF is employed to perform image smoothing on the probability maps. By using our JBF process, salt-and-pepper classification noise in homogeneous regions can be effectively smoothed out while object boundaries in the original image are better preserved as well. Third, a sequence of modified bi-labeling graph cut models is constructed for each information class to extract the desirable object belonging to the corresponding class from the smoothed probability maps. Finally, a classification map is achieved by merging the segmentation maps obtained in the last step using a simple and effective rule. Experimental results based on three benchmark airborne hyperspectral datasets with different resolutions and contexts demonstrate that our method can achieve 8.56%\u201313.68% higher overall accuracies than the pixel-wise SVM classifier. The performance of our method was further compared to several classical hyperspectral image classification methods using objective quantitative measures and a visual qualitative evaluation.<\/jats:p>","DOI":"10.3390\/rs8090748","type":"journal-article","created":{"date-parts":[[2016,9,12]],"date-time":"2016-09-12T10:24:41Z","timestamp":1473675881000},"page":"748","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model"],"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":"Haiwei","family":"Song","sequence":"additional","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"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,11]]},"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":"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":"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_5","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_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"2950","DOI":"10.1109\/TGRS.2006.876704","article-title":"A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery","volume":"44","author":"Zhang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","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_10","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_11","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_12","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/TGRS.2012.2209657","article-title":"Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features","volume":"51","author":"Qian","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1109\/JSTARS.2014.2308425","article-title":"Spectral-spatial classification of hyperspectral images using wavelets and extended morphological profiles","volume":"7","author":"Heras","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2962","DOI":"10.1109\/JSTARS.2015.2394778","article-title":"Wavelet-based classification of hyperspectral images using extended morphological profiles on graphics processing units","volume":"8","author":"Heras","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","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_16","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_17","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_18","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_19","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_20","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_21","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_22","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_23","doi-asserted-by":"crossref","first-page":"3780","DOI":"10.1109\/TGRS.2010.2049496","article-title":"Learning relevant image features with multiple kernel classification","volume":"48","author":"Tuia","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","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_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":"Mathieu","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TPAMI.2004.1262177","article-title":"What energy functions can be minimized via graph cuts?","volume":"26","author":"Kolmogorov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/34.57681","article-title":"Using dynamic programming for solving variational problems in vision","volume":"12","author":"Amini","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1016\/j.patcog.2012.09.015","article-title":"A survey of graph theoretical approaches to image segmentation","volume":"46","author":"Peng","year":"2013","journal-title":"Pattern Recognit."},{"unstructured":"Yu, X., Niu, R., Wang, Y., and Wu, K. (2009). Multispectral Image Processing and Pattern Recognition, SPIE.","key":"ref_29"},{"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.","key":"ref_30","DOI":"10.1109\/IGARSS.2014.6947216"},{"doi-asserted-by":"crossref","unstructured":"Ma, L., Ma, A., Ju, C., and Li, X. (2016). Graph-based semi-supervise d learning for spectral-spatial hyperspectral image classification. Pattern Recognit. Lett.","key":"ref_31","DOI":"10.1016\/j.patrec.2016.01.022"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/TGRS.2012.2205002","article-title":"A graph-based classification method for hyperspectral images","volume":"51","author":"Bai","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1109\/JSTARS.2015.2423278","article-title":"Spectral-spatial hyperspectral image classification using l1\/2 regularized low-rank representation and sparse representation-based graph cuts","volume":"8","author":"Jia","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/JSTARS.2015.2407493","article-title":"Dynamic ensemble selection approach for hyperspectral image classification with joint spectral and spatial information","volume":"8","author":"Damodaran","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"unstructured":"Tomasi, C., and Manduchi, R. (1998, January 4\u20137). Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision, Bombay, India.","key":"ref_35"},{"unstructured":"Shi, J., Fowlkes, C., Martin, D., and Sharon, E. Graph Based Image Segmentation Tutorial. Available online: http:\/\/www.cis.upenn.edu\/~jshi\/GraphTutorial\/.","key":"ref_36"},{"unstructured":"Ford, D.R., and Fulkerson, D.R. (2010). Flows in Networks, Princeton University Press.","key":"ref_37"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TPAMI.2004.60","article-title":"An experimental comparison of min-cut\/max-flow algorithms for energy minimization in vision","volume":"26","author":"Boykov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"Boykov, Y.Y., and Jolly, M.-P. (2001, January 13). Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. Proceedings of the Internation Conference on Computer Vision, Vancouver, BC, Canada.","key":"ref_39"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11263-006-7934-5","article-title":"Graph cuts and efficient n-d image segmentation","volume":"70","author":"Boykov","year":"2006","journal-title":"Int. J. Comput. Vis."},{"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.","key":"ref_41","DOI":"10.1145\/1961189.1961199"},{"key":"ref_42","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."},{"unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley.","key":"ref_43"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/0600000020","article-title":"Bilateral filtering: Theory and applications","volume":"4","author":"Paris","year":"2009","journal-title":"Found. Trends Comput. Graph. Vis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1145\/1015706.1015777","article-title":"Digital photography with flash and no-flash image pairs","volume":"23","author":"Petschnigg","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"Grabcut: Interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"ref_47","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_48","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."},{"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.","key":"ref_49","DOI":"10.1109\/WHISPERS.2009.5289072"},{"key":"ref_50","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."},{"unstructured":"Li, J. The Source Codes of the MLR-LORSAL, LBP-AL and LORSAR-AL-MLL Methods. Available online: http:\/\/www.lx.it.pt\/~jun.","key":"ref_51"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/9\/748\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:30:41Z","timestamp":1760211041000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/9\/748"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,11]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["rs8090748"],"URL":"https:\/\/doi.org\/10.3390\/rs8090748","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,9,11]]}}}