{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:54:55Z","timestamp":1769637295202,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,3,25]],"date-time":"2018-03-25T00:00:00Z","timestamp":1521936000000},"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>Semi-supervised classification methods result in higher performance for hyperspectral images, because they can utilize the relationship between unlabeled samples and labeled samples to obtain pseudo-labeled samples. However, how generating an effective training sample set is a major challenge for semi-supervised methods, In this paper, we propose a novel semi-supervised classification method based on extended label propagation (ELP) and a rolling guidance filter (RGF) called ELP-RGF, in which ELP is a new two-step process to make full use of unlabeled samples. The first step is to implement the graph-based label propagation algorithm to propagate the label information from labeled samples to the neighboring unlabeled samples. This is then followed by the second step, which uses superpixel propagation to assign the same labels to all pixels within the superpixels that are generated by the image segmentation method, so that some labels wrongly labeled by the above step can be modified. As a result, so obtained pseudo-labeled samples could be used to improve the performance of the classifier. Subsequently, an effective feature extraction method, i.e., RGF is further used to remove the noise and the small texture structures to optimize the features of the initial hyperspectral image. Finally, these produced initial labeled samples and high-confidence pseudo-labeled samples are used as a training set for support vector machine (SVM). The experimental results show that the proposed method can produce better classification performance for three widely-used real hyperspectral datasets, particularly when the number of training samples is relatively small.<\/jats:p>","DOI":"10.3390\/rs10040515","type":"journal-article","created":{"date-parts":[[2018,3,26]],"date-time":"2018-03-26T03:43:29Z","timestamp":1522035809000},"page":"515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering"],"prefix":"10.3390","volume":"10","author":[{"given":"Binge","family":"Cui","sequence":"first","affiliation":[{"name":"The College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong Province, China"}]},{"given":"Xiaoyun","family":"Xie","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong Province, China"}]},{"given":"Siyuan","family":"Hao","sequence":"additional","affiliation":[{"name":"The School of Information and Control Engineering, Qingdao University of Technology, No.777 Jialingjiang Road, Huangdao District, Qingdao 266520, Shandong Province, China"}]},{"given":"Jiandi","family":"Cui","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong Province, China"}]},{"given":"Yan","family":"Lu","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong Province, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/LGRS.2014.2375188","article-title":"Discriminant Tensor Spectral\u2013Spatial Feature Extraction for Hyperspectral Image Classification","volume":"12","author":"Zhong","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.isprsjprs.2016.12.010","article-title":"Multi-objective based spectral unmixing for hyperspectral images","volume":"124","author":"Xu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/TGRS.2013.2246837","article-title":"Sparse Transfer Manifold Embedding for Hyperspectral Target Detection","volume":"52","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","first-page":"1","article-title":"Sparse Transfer Manifold Embedding for Hyperspectral Target Detection","volume":"99","author":"Pan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","first-page":"1","article-title":"Hyperspectral Anomaly Detection with Attribute and Edge-Preserving Filters","volume":"99","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","unstructured":"Chang, C.I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Plenum Publishing Co."},{"key":"ref_7","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":"Inf. Theory IEEE Trans."},{"key":"ref_8","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":"Gotsis","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TGRS.1990.572944","article-title":"Neural Network Approaches versus Statistical Methods in Classification of Multisource Remote Sensing Data","volume":"28","author":"Benediktsson","year":"1989","journal-title":"Geosci. Remote Sens. Symp."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/LGRS.2013.2254108","article-title":"Hyperspectral Remote Sensing Image Classification Based on Rotation Forest","volume":"11","author":"Xia","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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_13","first-page":"1","article-title":"R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method","volume":"99","author":"Pan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","first-page":"1","article-title":"PCA-Based Edge-Preserving Features for Hyperspectral Image Classification","volume":"99","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pan, B., Shi, Z., and Xu, X. (2017). MugNet: Deep learning for hyperspectral image classification using limited samples. ISPRS J. Photogramm. Remote Sens.","DOI":"10.1016\/j.isprsjprs.2017.11.003"},{"key":"ref_16","first-page":"1","article-title":"Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images","volume":"99","author":"Pan","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","first-page":"1","article-title":"Hyperspectral Image Classification Based on Nonlinear Spectral\u2013Spatial Network","volume":"99","author":"Pan","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.patcog.2016.09.011","article-title":"Probabilistic Class Structure Regularized Sparse Representation Graph for Semi-Supervised Hyperspectral Image Classification","volume":"63","author":"Shao","year":"2017","journal-title":"Pat. Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1109\/TPAMI.2005.127","article-title":"Sparse multinomial logistic regression: Fast algorithms and generalization bounds","volume":"27","author":"Krishnapuram","year":"2005","journal-title":"IEEE Trans. Pat. Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ando, R.K., and Zhang, T. (2007, January 20\u201324). Two-view feature generation model for semi-supervised learning. Proceedings of the International Conference on Machine Learning, Corvalis, OR, USA.","DOI":"10.1145\/1273496.1273500"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/TMM.2016.2515367","article-title":"Semi-Supervised Bi-Dictionary Learning for Image Classification With Smooth Representation-Based Label Propagation","volume":"18","author":"Meng","year":"2016","journal-title":"IEEE Transa. Multimedia."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TKDE.2007.190672","article-title":"Label Propagation through Linear Neighborhoods","volume":"20","author":"Wang","year":"2008","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.isprsjprs.2014.08.016","article-title":"Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation","volume":"97","author":"Wang","year":"2014","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_25","unstructured":"Zhu, X., Ghahramani, Z., and Mit, T.J. (, January January). Semi-Supervised Learning with Graphs. Proceedings of the International Joint Conference on Natural Language Processing, Carnegie Mellon University, Pittsburgh, PA, USA."},{"key":"ref_26","unstructured":"Cheng, H., and Liu, Z. (October, January 29). Sparsity induced similarity measure for label propagation. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1109\/TNNLS.2013.2271327","article-title":"Multiple Graph Label Propagation by Sparse Integration","volume":"24","author":"Karasuyama","year":"2013","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_28","unstructured":"Zhu, X., and Ghahramani, Z. (2003, January 21\u201324). Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. Proceedings of the Twentieth International Conference on International Conference on Machine Learning, Washington, DC, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Moore, A.P., Prince, S.J.D., Warrell, J., and Mohammed, U. (2008, January 23\u201328). Superpixel lattices. Proceedings of the Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587471"},{"key":"ref_30","first-page":"213","article-title":"Spectral-spatial multi-feature-based deep learning for hyperspectral remote sensing image classification","volume":"21","author":"Wang","year":"2017","journal-title":"Soft Comput. A Fusion Found. Methodol. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, S., Fu, W., and Fang, L. (2017). Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9020139"},{"key":"ref_32","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and S\u00fcsstrunk, S. (2010). SLIC Superpixels, EPFL."},{"key":"ref_33","unstructured":"De Carvalho, M.A.G., da Costa, A.L., Ferreira, A.C.B., and Junior, R.M.C. (September, January 30). Image Segmentation Using Watershed and Normalized Cut. Proceedings of the SIBIGRAPI, Gramado, Brazil."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1109\/TIP.2009.2025555","article-title":"Automatic image segmentation by dynamic region growth and multiresolution merging","volume":"18","author":"Ugarriza","year":"2009","journal-title":"IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc."},{"key":"ref_35","first-page":"1","article-title":"Superpixel-Based Intrinsic Image Decomposition of Hyperspectral Images","volume":"99","author":"Jin","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","first-page":"1","article-title":"Superpixel-Based Multitask Learning Framework for Hyperspectral Image Classification","volume":"99","author":"Jia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","unstructured":"Zhang, D., Yang, Y., and Song, K. Research on a Multi-Scale Segmentation Algorithm Based on High Resolution Satellite Remote Sensing Image, International Conference on Intelligent Control and Computer Application."},{"key":"ref_38","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"Belkin","year":"2006","journal-title":"Mach. Learn. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Shen, X., and Xu, L. (2014, January 6\u201312). Rolling Guidance Filter. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9_53"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/j.patcog.2011.09.001","article-title":"Supervised neighborhood graph construction for semi-supervised classification","volume":"4","author":"Rohban","year":"2012","journal-title":"Pat. Recognit."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1109\/TNN.1997.641482","article-title":"The Nature of Statistical Learning Theory","volume":"8","author":"Vapnik","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_42","first-page":"571","article-title":"The research of simplification of structure of multiclass classifier of support vector machine","volume":"5","author":"Wang","year":"2005","journal-title":"Image Graph."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TGRS.2014.2319373","article-title":"Extended Random Walker-Based Classification of Hyperspectral Images","volume":"53","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TGRS.2013.2264508","article-title":"Spectral\u2013Spatial Hyperspectral Image Classification With Edge-Preserving Filtering","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/515\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:58:27Z","timestamp":1760194707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/515"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,25]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040515"],"URL":"https:\/\/doi.org\/10.3390\/rs10040515","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,25]]}}}