{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T09:05:09Z","timestamp":1768035909036,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:00:00Z","timestamp":1650412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Mine Water Resource Utilization of Anhui Higher Education Institutes","award":["KMWRU202107"],"award-info":[{"award-number":["KMWRU202107"]}]},{"name":"Key Natural Science Project of the Anhui Provincial Education Department","award":["KJ2021ZD0137"],"award-info":[{"award-number":["KJ2021ZD0137"]}]},{"name":"Key Natural Science Project of the Anhui Provincial Education Department","award":["KJ2020A0733"],"award-info":[{"award-number":["KJ2020A0733"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The hyperspectral feature extraction technique is one of the most popular topics in the remote sensing community. However, most hyperspectral feature extraction methods are based on region-based local information descriptors while neglecting the correlation and dependencies of different homogeneous regions. To alleviate this issue, this paper proposes a multi-view structural feature extraction method to furnish a complete characterization for spectral\u2013spatial structures of different objects, which mainly is made up of the following key steps. First, the spectral number of the original image is reduced with the minimum noise fraction (MNF) method, and a relative total variation is exploited to extract the local structural feature from the dimension reduced data. Then, with the help of a superpixel segmentation technique, the nonlocal structural features from intra-view and inter-view are constructed by considering the intra- and inter-similarities of superpixels. Finally, the local and nonlocal structural features are merged together to form the final image features for classification. Experiments on several real hyperspectral datasets indicate that the proposed method outperforms other state-of-the-art classification methods in terms of visual performance and objective results, especially when the number of training set is limited.<\/jats:p>","DOI":"10.3390\/rs14091971","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:22:43Z","timestamp":1650414163000},"page":"1971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Multi-View Structural Feature Extraction for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Nannan","family":"Liang","sequence":"first","affiliation":[{"name":"School of Informatics and Engineering, Suzhou University, Suzhou 234000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Puhong","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Informatics and Engineering, Suzhou University, Suzhou 234000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Informatics and Engineering, Suzhou University, Suzhou 234000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Informatics and Engineering, Suzhou University, Suzhou 234000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MGRS.2020.2979764","article-title":"Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox","volume":"8","author":"Rasti","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Duan, P., Lai, J., Ghamisi, P., Kang, X., Jackisch, R., Kang, J., and Gloaguen, R. (2020). Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12182903"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.patcog.2017.11.024","article-title":"Material Based Salient Object Detection from Hyperspectral Images","volume":"76","author":"Liang","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1109\/TGRS.2019.2936308","article-title":"Hyperspectral Anomaly Detection With Kernel Isolation Forest","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.isprsjprs.2020.06.009","article-title":"Texture-Aware Total Variation-Based Removal of Sun Glint in Hyperspectral Images","volume":"166","author":"Duan","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Stuart, M.B., McGonigle, A.J.S., and Willmott, J.R. (2019). Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors, 19.","DOI":"10.3390\/s19143071"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TGRS.2017.2743102","article-title":"PCA-Based Edge-Preserving Features for Hyperspectral Image Classification","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TGRS.2005.863297","article-title":"Independent Component Analysis-Based Dimensionality Reduction with Applications in Hyperspectral Image Analysis","volume":"44","author":"Wang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gao, L., Zhao, B., Jia, X., Liao, W., and Zhang, B. (2017). Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9060548"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1080\/2150704X.2018.1524993","article-title":"Feature Extraction from Hyperspectral Images using Learned Edge Structures","volume":"10","author":"Zhang","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_12","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_13","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":"Villa","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3742","DOI":"10.1109\/TGRS.2013.2275613","article-title":"Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/JSTARS.2019.2915272","article-title":"Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation","volume":"12","author":"Duan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7726","DOI":"10.1109\/TGRS.2020.3031928","article-title":"Fusion of Dual Spatial Information for Hyperspectral Image Classification","volume":"59","author":"Duan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4971","DOI":"10.1109\/TGRS.2016.2553842","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Images Using ICA and Edge-Preserving Filter via an Ensemble Strategy","volume":"54","author":"Xia","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cui, B., Xie, X., Hao, S., Cui, J., and Lu, Y. (2018). Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering. Remote Sens., 10.","DOI":"10.3390\/rs10040515"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4180","DOI":"10.1109\/TGRS.2019.2961599","article-title":"Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification","volume":"58","author":"Sellars","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4976","DOI":"10.1109\/TGRS.2020.2971081","article-title":"Subpixel-Pixel-Superpixel-Based Multiview Active Learning for Hyperspectral Images Classification","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"61793","DOI":"10.1109\/ACCESS.2021.3074405","article-title":"Spectral-Spatial Active Learning With Structure Density for Hyperspectral Classification","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/TGRS.2017.2769673","article-title":"Supervised Deep Feature Extraction for Hyperspectral Image Classification","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/LGRS.2018.2873476","article-title":"Dual-Path Network-Based Hyperspectral Image Classification","volume":"16","author":"Kang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","first-page":"3232","article-title":"Multi-Layer Global Spectral-Spatial Attention Network for Wetland Hyperspectral Image Classification","volume":"58","author":"Xie","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TGRS.2020.3007921","article-title":"Hyperspectral Image Classification With Attention-Aided CNNs","volume":"59","author":"Hang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3172371","article-title":"SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers","volume":"60","author":"Hong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s11431-021-1989-9","article-title":"Self-Supervised Learning-Based Oil Spill Detection of Hyperspectral Images","volume":"65","author":"Duan","year":"2022","journal-title":"Sci. China Technol. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1109\/TGRS.2017.2752738","article-title":"Multiview Intensity-Based Active Learning for Hyperspectral Image Classification","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1109\/JSTARS.2016.2552998","article-title":"Wavelet-Domain Multiview Active Learning for Spatial-Spectral Hyperspectral Image Classification","volume":"9","author":"Zhou","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A Transformation for Ordering Multispectral Data in terms of Image Quality with Implications for Noise Removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"1","article-title":"Structure Extraction from Texture via Relative Total Variation","volume":"31","author":"Xu","year":"2012","journal-title":"ACM Trans. Graph."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/TPAMI.2013.107","article-title":"Entropy-Rate Clustering: Cluster Analysis via Maximizing a Submodular Function Subject to a Matroid Constraint","volume":"36","author":"Liu","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gerstner, W., Germond, A., Hasler, M., and Nicoud, J.D. (1997). Artificial Neural Networks\u2014ICANN\u201997, Proceedings of the 7th International Conference, Lausanne, Switzerland, 8\u201310 October 1997 Proceeedings, Springer.","DOI":"10.1007\/BFb0020124"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10336","DOI":"10.1109\/TGRS.2019.2933588","article-title":"Fusion of Multiple Edge-Preserving Operations for Hyperspectral Image Classification","volume":"57","author":"Duan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.1109\/TGRS.2019.2949427","article-title":"Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization","volume":"58","author":"Duan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded Recurrent Neural Networks for Hyperspectral Image Classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of Hyperspectral Images by Exploiting Spectral\u2013Spatial Information of Superpixel via Multiple Kernels","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1109\/TGRS.2019.2944989","article-title":"Generalized Tensor Regression for Hyperspectral Image Classification","volume":"58","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1109\/TGRS.2016.2623742","article-title":"Discriminative Low-Rank Gabor Filtering for Spectral\u2013Spatial Hyperspectral Image Classification","volume":"55","author":"He","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TGRS.2014.2358615","article-title":"Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images","volume":"53","author":"Kang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3791","DOI":"10.1109\/TGRS.2019.2957251","article-title":"Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification","volume":"58","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6976","DOI":"10.1109\/TGRS.2016.2593463","article-title":"Hyperspectral Feature Extraction Using Total Variation Component Analysis","volume":"54","author":"Rasti","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.isprsjprs.2018.05.014","article-title":"Hyperspectral Image Classification via a Random Patches Network","volume":"142","author":"Xu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/1971\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:57:06Z","timestamp":1760137026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/1971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,20]]},"references-count":46,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14091971"],"URL":"https:\/\/doi.org\/10.3390\/rs14091971","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,20]]}}}