{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:25:14Z","timestamp":1776443114193,"version":"3.51.2"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Chinese Academy of Sciences (CAS)","award":["Y9U933T190"],"award-info":[{"award-number":["Y9U933T190"]}]},{"name":"International Cooperation Project of Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP)","award":["Y9U933T190"],"award-info":[{"award-number":["Y9U933T190"]}]},{"name":"Dazhi Scholarship of the Guangdong Polytechnic Normal University","award":["Y9U933T190"],"award-info":[{"award-number":["Y9U933T190"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15\u201320% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online.<\/jats:p>","DOI":"10.3390\/rs15040890","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T05:29:05Z","timestamp":1675661345000},"page":"890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8921-1493","authenticated-orcid":false,"given":"Qiaoyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Precision Machinery and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Donglin","family":"Xue","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Precision Machinery and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Yanhui","family":"Tang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Precision Machinery and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Yongxian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Precision Machinery and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Jinchang","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computing Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China"},{"name":"National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK"}]},{"given":"Haijiang","family":"Sun","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Precision Machinery and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"ref_1","first-page":"730","article-title":"An assessment of independent component analysis for detection of military targets from hyperspectral images","volume":"13","author":"Tiwari","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","article-title":"Hyperspectral imaging for military and security applications: Combining myriad processing and sensing techniques","volume":"7","author":"Shimoni","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Magaz."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7363","DOI":"10.1080\/2150704X.2014.968681","article-title":"A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index","volume":"35","author":"Jiao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","first-page":"1","article-title":"Hyperspectral forest monitoring and imaging implications","volume":"Volume 9104","author":"David","year":"2014","journal-title":"Spectral Imaging Sensor Technologies: Innovation Driving Advanced Application Capabilities"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112029","DOI":"10.1016\/j.postharvbio.2022.112029","article-title":"Nondestructive detection and grading of flesh translucency in pineapples with visible and near-infrared spectroscopy","volume":"192","author":"Xu","year":"2022","journal-title":"Postharvest Biol. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3229248","article-title":"Non-Destructive Testing of Composite Fiber Materials with Hyperspectral Imaging\u2014Evaluative Studies in the EU H2020 FibreEUse Project","volume":"71","author":"Yan","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ge, X., Ding, J., Jin, X., Wang, J., Chen, X., Li, X., Liu, J., and Xie, B. (2021). Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region. Remote Sens., 13.","DOI":"10.3390\/rs13081562"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/JSTARS.2015.2406339","article-title":"Generation of Spectral\u2014Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications","volume":"8","author":"Gevaert","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gevaert, C.M., Tang, J., Garc\u00eda-Haro, F.J., Suomalainen, J., and Kooistra, L. (2014, January 24\u201327). Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications. Proceedings of the Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan.","DOI":"10.1109\/WHISPERS.2014.8077607"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3672","DOI":"10.1109\/TGRS.2016.2524557","article-title":"Spectral\u2013Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/BF01588971","article-title":"Ananalysis of the approximations for maximizing submodular set functions","volume":"14","author":"Nemhauser","year":"1978","journal-title":"Math. Program."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/LGRS.2017.2746625","article-title":"Fast Spectral Clustering with Anchor Graph for Large Hyperspectral Images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/LGRS.2020.2985981","article-title":"Hypergraph-Regularized Low-Rank Subspace Clustering Using Superpixels for Unsupervised Spatial\u2013Spectral Hyperspectral Classification","volume":"18","author":"Xu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","first-page":"580","article-title":"A fuzzy K-nearest neighbor algorithm","volume":"15","author":"Keller","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"6158","DOI":"10.1109\/TCYB.2021.3104100","article-title":"SpaSSA: Superpixelwise Adaptive SSA for Unsupervised Spatial\u2013Spectral Feature Extraction in Hyperspectral Image","volume":"52","author":"Sun","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10773","DOI":"10.1109\/JSTARS.2021.3120071","article-title":"A Fast and Accurate Similarity-Constrained Subspace Clustering Algorithm for Hyperspectral Image","volume":"14","author":"Hinojosa","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5501813","DOI":"10.1109\/TGRS.2021.3057768","article-title":"Self-Supervised Learning with Adaptive Distillation for Hyperspectral Image Classification","volume":"60","author":"Yue","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Anindya, I.C., and Kantarcioglu, M. (2018, January 6\u20139). Adversarial anomaly detection using centroid-based clustering. Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration (IRI), Lake City, UT, USA.","DOI":"10.1109\/IRI.2018.00009"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1109\/TGRS.2005.861548","article-title":"An unsupervised artificial immune classifier for multi\/hyperspectral remote sensing imagery","volume":"44","author":"Zhong","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/TPAMI.2010.88","article-title":"Parallel Spectral Clustering in Distributed Systems","volume":"33","author":"Chen","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nie, F., Wang, X., Jordan, M., and Huang, H. (2016, January 12\u201317). The constrained Laplacian rank algorithm for graph-based clustering. Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10302"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, T.-N., Lee, C.-J., and Yen, S.-J. (2009, January 20\u201324). Fuzzy objective functions for robust pattern recognition. Proceedings of the 2009 IEEE International Conference on Fuzzy Systems, Jeju Island, Republic of Korea.","DOI":"10.1109\/FUZZY.2009.5277269"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5461","DOI":"10.1080\/01431161.2010.502155","article-title":"Unsupervised remote sensing image classification using an artificial immune network","volume":"32","author":"Zhong","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/JSTARS.2013.2240655","article-title":"Automatic Fuzzy Clustering Based on Adaptive Multi-Objective Differential Evolution for Remote Sensing Imagery","volume":"6","author":"Zhong","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, L., and You, J. (2017, January 6\u20138). A spectral clustering based method for hyperspectral urban image. Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates.","DOI":"10.1109\/JURSE.2017.7924602"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.neucom.2018.08.059","article-title":"Spectral clustering based on iterative optimization for large-scale and high-dimensional data","volume":"318","author":"Zhao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1109\/TNNLS.2018.2861209","article-title":"Spectral Embedded Adaptive Neighbors Clustering","volume":"30","author":"Wang","year":"2019","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Yuan, Y., and Wang, Q. (2019). Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11040399"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5798","DOI":"10.1109\/TGRS.2017.2714676","article-title":"Two-Stage Reranking for Remote Sensing Image Retrieval","volume":"55","author":"Tang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bergh, M.V.D., Boix, X., Roig, G., de Capitani, B., and Van Gool, L. (2012, January 7\u201313). SEEDS: Superpixels Extracted via Energy-Driven Sampling. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33786-4_2"},{"key":"ref_34","unstructured":"Li, Z., and Chen, J. (2015, January 7\u201312). Superpixel segmentation using linear spectral clustering. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zhao, L., and Ren, L. (2019, January 19\u201321). Different Versions of Entropy Rate Superpixel Segmentation for Hyperspectral Image. Proceedings of the International Conference on Signal and Image Processing, Wuxi, China.","DOI":"10.1109\/SIPROCESS.2019.8868344"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4418","DOI":"10.1109\/TGRS.2015.2398468","article-title":"Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging","volume":"53","author":"Zabalza","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","first-page":"1","article-title":"Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery","volume":"60","author":"Fu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1109\/JSTARS.2020.3040699","article-title":"Multiscale 2-D Singular Spectrum Analysis and Principal Component Analysis for Spatial\u2013Spectral Noise-Robust Feature Extraction and Classification of Hyperspectral Images","volume":"14","author":"Ma","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"419","DOI":"10.4310\/SII.2010.v3.n3.a14","article-title":"Singular spectrum analysis for image processing","volume":"3","author":"Zhigljavsky","year":"2010","journal-title":"Stat. Its Interface"},{"key":"ref_40","first-page":"405","article-title":"A review of nonnegative matrix factorization methods for clustering","volume":"1","year":"2015","journal-title":"Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5998","DOI":"10.1109\/TGRS.2019.2961703","article-title":"Subpixel-Pixel-Superpixel Guided Fusion for Hyperspectral Anomaly Detection","volume":"58","author":"Huang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","unstructured":"(2021, July 12). Hyperspectral Remote Sensing Scenes. Available online: http:\/\/www.ehu.es\/ccwintco\/index.php?title=Hyperspectral_Remote_Sensing_Scenes."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"71632","DOI":"10.1109\/ACCESS.2018.2879963","article-title":"Unsupervised Band Selection of Hyperspectral Images via Multi-Dictionary Sparse Representation","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging Spectroscopy and the Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Xie, F., Lei, C., Jin, C., and An, N. (2020). A Novel Spectral\u2013Spatial Classification Method for Hyperspectral Image at Superpixel Level. Appl. Sci., 10.","DOI":"10.3390\/app10020463"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2007","journal-title":"ACM Trans. Intell. Syst. Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/890\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:25:29Z","timestamp":1760120729000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/890"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,6]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040890"],"URL":"https:\/\/doi.org\/10.3390\/rs15040890","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,6]]}}}