{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T02:27:33Z","timestamp":1771381653916,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,20]],"date-time":"2018-06-20T00:00:00Z","timestamp":1529452800000},"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":["61403351 and 61402424"],"award-info":[{"award-number":["61403351 and 61402424"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Qinzhou scientific research and technology development plan project","award":["201714322"],"award-info":[{"award-number":["201714322"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, image-filtering based hyperspectral image (HSI) feature extraction has been widely studied. However, due to limited spatial resolution and feature distribution complexity, the problems of cross-region mixing after filtering and spectral discriminative reduction still remain. To address these issues, this paper proposes a spectral-spatial propagation filter (PF) based HSI feature extraction method that can effectively address the above problems. The dimensionality\/band of an HSI is typically high; therefore, principal component analysis (PCA) is first used to reduce the HSI dimensionality. Then, the principal components of the HSI are filtered with the PF. When cross-region mixture occurs in the image, the filter template reduces the weight assignments of the cross-region mixed pixels to handle the issue of cross-region mixed pixels simply and effectively. To validate the effectiveness of the proposed method, experiments are carried out on three common HSIs using support vector machine (SVM) classifiers with features learned by the PF. The experimental results demonstrate that the proposed method effectively extracts the spectral-spatial features of HSIs and significantly improves the accuracy of HSI classification.<\/jats:p>","DOI":"10.3390\/s18061978","type":"journal-article","created":{"date-parts":[[2018,6,20]],"date-time":"2018-06-20T10:41:24Z","timestamp":1529491284000},"page":"1978","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Spectral-Spatial Feature Extraction of Hyperspectral Images Based on Propagation Filter"],"prefix":"10.3390","volume":"18","author":[{"given":"Zhikun","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Beibu Gulf Big Data Resources Utilisation Lab, Qinzhou University, Qinzhou 535000, China"},{"name":"Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Qinzhou 535000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjun","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwei","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoping","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0020-6503","authenticated-orcid":false,"given":"Zhihua","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Beibu Gulf Big Data Resources Utilisation Lab, Qinzhou University, Qinzhou 535000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,20]]},"reference":[{"key":"ref_1","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_2","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_3","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.3390\/s17051124","article-title":"Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data","volume":"17","author":"Han","year":"2017","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1109\/TGRS.2015.2501425","article-title":"Retrieval of the Ocean Skin Temperature Profiles From Measurements of Infrared Hyperspectral Radiometers\u2014Part II: Field Data Analysis","volume":"54","author":"Wong","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, T., Wei, W., and Zhao, B. (2018). A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds. Sensors, 18.","DOI":"10.3390\/s18030813"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Behmann, J., Acebron, K., and Emin, D. (2018). Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors, 18.","DOI":"10.3390\/s18020441"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sandino, J., Pegg, G., and Gonzalez, F. (2018). Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence. Sensors, 18.","DOI":"10.3390\/s18040944"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ma, N., Peng, Y., and Wang, S. (2018). An Unsupervised Deep Hyperspectral Anomaly Detector. Sensors, 18.","DOI":"10.3390\/s18030693"},{"key":"ref_9","first-page":"99","article-title":"Hyperspectral Image Classification in the Presence of Noisy Labels","volume":"1","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ma, J., Jiang, J., Zhou, H., Zhao, J., and Guo, X. (2018). Guided Locality Preserving Feature Matching for Remote Sensing Image Registration. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2018.2820040"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","article-title":"Feature Selection for Classification of Hyperspectral Data by SVM","volume":"48","author":"Pal","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huo, H., Guo, J., and Li, Z. (2018). Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach. Sensors, 18.","DOI":"10.3390\/s18020363"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tong, F., Tong, H., Jiang, J., and Zhang, Y. (2017). Multiscale union regions adaptive sparse representation for hyperspectral image classification. Remote Sens., 9.","DOI":"10.3390\/rs9090872"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.ins.2017.07.010","article-title":"Feature guided Gaussian mixture model with semi-supervised EM and local geometric constraint for retinal image registration","volume":"417","author":"Ma","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","article-title":"Infrared and visible image fusion via gradient transfer and total variation minimization","volume":"31","author":"Ma","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","article-title":"Infrared and visible image fusion methods and applications: A survey","volume":"45","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_17","first-page":"121","article-title":"Supervised Topic Models","volume":"20","author":"Jon","year":"2008","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2017.2734680","article-title":"Supervised Gaussian Process Latent Variable Model for Hyperspectral Image Classification","volume":"14","author":"Jiang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18837","DOI":"10.3390\/s141018837","article-title":"Detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system","volume":"14","author":"Lee","year":"2014","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1109\/TGRS.2008.2008308","article-title":"Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification","volume":"47","author":"Kuo","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","unstructured":"Jolliffe, I. (2002). Principal Component Analysis, Springer. [2nd ed.]."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Boukhechba, K., Wu, H., and Bazine, R. (2018). DCT-Based Preprocessing Approach for ICA in Hyperspectral Data Analysis. Sensors, 18.","DOI":"10.3390\/s18041138"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, J., Ma, J., Chen, C., Wang, Z., Cai, Z., and Wang, L. (2018). SuperPCA: A Superpixelwise Principal Component Analysis Approach for Unsupervised Feature Extraction of Hyperspectral Imagery. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2018.2828029"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.1016\/j.patcog.2008.01.001","article-title":"A unified framework for semi-supervised dimensionality reduction","volume":"41","author":"Song","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1109\/JSTARS.2013.2295610","article-title":"Spectral\u2013spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/LGRS.2016.2645708","article-title":"Spatial-Aware Collaborative Representation for Hyperspectral Remote Sensing Image Classification","volume":"14","author":"Jiang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/JSTARS.2013.2295313","article-title":"Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification","volume":"7","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","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."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TCYB.2015.2453359","article-title":"Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification","volume":"46","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wei, Y., Zhou, Y., and Li, H. (2017). Spectral-Spatial Response for Hyperspectral Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9030203"},{"key":"ref_33","unstructured":"Yu, S., Liang, X., and Molaei, M. (2016, January 20\u201324). Joint Multiview Fused ELM Learning with Propagation Filter for Hyperspectral Image Classification. Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chang, J., and Wang, Y. (2015, January 7\u201312). Propagated image filtering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognit, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298595"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2012.2205263","article-title":"Spectral\u2013Spatial 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_36","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","article-title":"Limitations of Principal Components Analysis for Hyperspectral Target Recognition","volume":"5","author":"Prasad","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","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_39","first-page":"27","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1109\/LGRS.2016.2608963","article-title":"Hyperspectral Image Classification Based on Nonlinear Spectral\u2013Spatial Network","volume":"13","author":"Pan","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Data Based on Deep Belief Network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/6\/1978\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:09:28Z","timestamp":1760195368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/6\/1978"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,20]]},"references-count":42,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["s18061978"],"URL":"https:\/\/doi.org\/10.3390\/s18061978","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,20]]}}}