{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T09:45:32Z","timestamp":1784454332496,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T00:00:00Z","timestamp":1725062400000},"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>This study focused on improving the clustering performance of hyperspectral imaging (HSI) by employing the Generalized Orthogonal Matching Pursuit (GOMP) algorithm for feature extraction. Hyperspectral remote sensing imaging technology, which is crucial in various fields like environmental monitoring and agriculture, faces challenges due to its high dimensionality and complexity. Supervised learning methods require extensive data and computational resources, while clustering, an unsupervised method, offers a more efficient alternative. This research presents a novel approach using GOMP to enhance clustering performance in HSI. The GOMP algorithm iteratively selects multiple dictionary elements for sparse representation, which makes it well-suited for handling complex HSI data. The proposed method was tested on two publicly available HSI datasets and evaluated in comparison with other methods to demonstrate its effectiveness in enhancing clustering performance.<\/jats:p>","DOI":"10.3390\/rs16173230","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:59:40Z","timestamp":1725263980000},"page":"3230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Clustering Hyperspectral Imagery via Sparse Representation Features of the Generalized Orthogonal Matching Pursuit"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8480-1603","authenticated-orcid":false,"given":"Wenqi","family":"Guo","sequence":"first","affiliation":[{"name":"School of Science, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Science, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqiang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Information, Northeast Agricultural University, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shichen","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Science, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zibu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Science, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"147758","DOI":"10.1016\/j.scitotenv.2021.147758","article-title":"Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review","volume":"788","author":"Lassalle","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112322","DOI":"10.1016\/j.rse.2021.112322","article-title":"Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks","volume":"256","author":"Mayra","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pour, A.B., Guha, A., Crispini, L., and Chatterjee, S. (2023). Editorial for the Special Issue Entitled Hyperspectral Remote Sensing from Spaceborne and Low-Altitude Aerial\/Drone-Based Platforms\u2014Differences in Approaches, Data Processing Methods, and Applications. Remote Sens., 15.","DOI":"10.3390\/rs15215119"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5518615","DOI":"10.1109\/TGRS.2021.3130716","article-title":"SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers","volume":"60","author":"Hong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"82","article-title":"Strategies for dimensionality reduction in hyperspectral remote sensing: A comprehensive overview","volume":"27","author":"Vaddi","year":"2024","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1109\/LGRS.2019.2924934","article-title":"Correntropy-Based Sparse Spectral Clustering for Hyperspectral Band Selection","volume":"17","author":"Sun","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ins.2019.02.008","article-title":"Hyperspectral image unsupervised classification by robust manifold matrix factorization","volume":"485","author":"Zhang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kong, X., Fu, M., Zhao, X., Wang, J., and Jiang, P. (2021). Ecological effects of land-use change on two sides of the Hu Huanyong Line in China. Land Use Policy.","DOI":"10.1016\/j.landusepol.2021.105895"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Q., Mu, T., Gong, H., Dai, H., Li, C., He, Z., Wang, W., Han, F., Tuniyazi, A., and Li, H. (2022). A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection. Remote Sens., 14.","DOI":"10.3390\/rs14122838"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1186\/s13634-022-00926-8","article-title":"Multilayer graph spectral analysis for hyperspectral images","volume":"2022","author":"Zhang","year":"2022","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_13","first-page":"5516712","article-title":"Marginalized Graph Self-Representation for Unsupervised Hyperspectral Band Selection","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, Q., Xue, D., Tang, Y., Zhao, Y., Ren, J., and Sun, H. (2023). PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15040890"},{"key":"ref_15","first-page":"5526116","article-title":"EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3509105","DOI":"10.1109\/LGRS.2023.3316732","article-title":"Graph Guided Transformer: An Image-Based Global Learning Framework for Hyperspectral Image Classification","volume":"20","author":"Shi","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, K., Zhan, Y., An, Y., and Li, S. (2024). Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification. Remote Sens., 16.","DOI":"10.3390\/rs16132328"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4703612","DOI":"10.1109\/TGRS.2021.3112298","article-title":"Semi-Supervised Superpixel-Based Multi-Feature Graph Learning for Hyperspectral Image Data","volume":"60","author":"Kotzagiannidis","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","article-title":"Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit","volume":"53","author":"Tropp","year":"2007","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7502205","DOI":"10.1109\/LGRS.2023.3264623","article-title":"High-Dimensional Generalized Orthogonal Matching Pursuit With Singular Value Decomposition","volume":"20","author":"Zong","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Justo, J.A., and Orlandi\u0107, M. (2022, January 13\u201316). Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images. Proceedings of the 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Rome, Italy.","DOI":"10.1109\/WHISPERS56178.2022.9955118"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8013405","DOI":"10.1109\/LGRS.2021.3086492","article-title":"Generalized Orthogonal Matching Pursuit With Singular Value Decomposition","volume":"19","author":"Fu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230829","article-title":"SC-EADNet: A Self-Supervised Contrastive Efficient Asymmetric Dilated Network for Hyperspectral Image Classification","volume":"60","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alkhatib, M.Q., Al-Saad, M., Aburaed, N., Almansoori, S., Zabalza, J., Marshall, S., and Al-Ahmad, H. (2023). Tri-CNN: A Three Branch Model for Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15020316"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","article-title":"Billion-Scale Similarity Search with GPUs","volume":"7","author":"Johnson","year":"2021","journal-title":"IEEE Trans. Big Data"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5085","DOI":"10.1109\/TGRS.2020.3018879","article-title":"Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhai, H., Zhang, H., Xu, X., Zhang, L., and Li, P. (2017). Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation. Remote Sens., 9.","DOI":"10.3390\/rs9040335"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN Feature Hierarchy for Hyperspectral Image Classification","volume":"17","author":"Roy","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103296","DOI":"10.1016\/j.infrared.2020.103296","article-title":"Hyperspectral image classification using CNN with spectral and spatial features integration","volume":"107","author":"Vaddi","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/j.ymssp.2018.12.054","article-title":"Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis","volume":"122","author":"Sun","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TII.2019.2909305","article-title":"Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary","volume":"16","author":"Qin","year":"2020","journal-title":"IEEE Trans. Ind. Informa."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Celebi, M.E., Kingravi, H.A., and Vela, P.A. (2012). A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm. ArXiv.","DOI":"10.1016\/j.eswa.2012.07.021"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1086\/304888","article-title":"A Universal density profile from hierarchical clustering","volume":"490","author":"Navarro","year":"1997","journal-title":"Astrophys. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1109\/TPAMI.2013.57","article-title":"Sparse Subspace Clustering: Algorithm, Theory, and Applications","volume":"35","author":"Elhamifar","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Seraj, R., and Islam, S.M.S. (2020). The k-means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics, 9.","DOI":"10.3390\/electronics9081295"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1109\/TNN.2008.2005601","article-title":"Normalized Mutual Information Feature Selection","volume":"20","author":"Estevez","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., Jones, A., Vreys, K., Bomans, B., and Van Wesemael, B. (2018). Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database. Remote Sens., 10.","DOI":"10.3390\/rs10020153"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"Scikit-image: Image Processing in Python","volume":"2","author":"Gomez","year":"2014","journal-title":"PeerJ"},{"key":"ref_40","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1038\/s41592-018-0308-4","article-title":"Fast Interpolation-based t-SNE for Improved Visualization of Single-Cell RNA-Seq Data","volume":"16","author":"Linderman","year":"2017","journal-title":"Nat. Methods"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Guo, W., Zhang, W., Zhang, Z., Tang, P., and Gao, S. (2022). Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis. Remote Sens., 14.","DOI":"10.3390\/rs14153635"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3230\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:46:14Z","timestamp":1760111174000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,31]]},"references-count":42,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173230"],"URL":"https:\/\/doi.org\/10.3390\/rs16173230","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,31]]}}}