{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:35:50Z","timestamp":1760142950498,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["31400","2019QZKK0206"],"award-info":[{"award-number":["31400","2019QZKK0206"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["31400","2019QZKK0206"],"award-info":[{"award-number":["31400","2019QZKK0206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Denoising serves as a critical preprocessing step for the subsequent analysis of the hyperspectral image (HSI). Due to their high computational efficiency, low-rank-based denoising methods that project the noisy HSI into a low-dimensional subspace identified by certain criteria have gained widespread use. However, methods employing second-order statistics as criteria often struggle to retain the signal of the small targets in the denoising results. Other methods utilizing high-order statistics encounter difficulties in effectively suppressing noise. To tackle these challenges, we delve into a novel criterion to determine the projection subspace, and propose an innovative low-rank-based method that successfully preserves the spectral characteristic of small targets while significantly reducing noise. The experimental results on the synthetic and real datasets demonstrate the effectiveness of the proposed method, in terms of both small-target preservation and noise reduction.<\/jats:p>","DOI":"10.3390\/rs16020276","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T05:47:21Z","timestamp":1704865641000},"page":"276","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hyperspectral Image Denoising Based on Principal-Third-Order-Moment Analysis"],"prefix":"10.3390","volume":"16","author":[{"given":"Shouzhi","family":"Li","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiurui","family":"Geng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Liangliang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 242099, China"}]},{"given":"Luyan","family":"Ji","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Yongchao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6158","DOI":"10.1109\/TCYB.2021.3104100","article-title":"SpaSSA: Superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image","volume":"52","author":"Sun","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_2","first-page":"1","article-title":"Subspace clustering for hyperspectral images via dictionary learning with adaptive regularization","volume":"60","author":"Huang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hennessy, A., Clarke, K., and Lewis, M. (2020). Hyperspectral classification of plants: A review of waveband selection generalisability. Remote Sens., 12.","DOI":"10.3390\/rs12010113"},{"key":"ref_4","first-page":"79","article-title":"Hyperspectral image processing for automatic target detection applications","volume":"14","author":"Manolakis","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.1109\/TGRS.2020.3024852","article-title":"Target detection through tree-structured encoding for hyperspectral images","volume":"59","author":"Sun","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/JSTARS.2019.2902430","article-title":"Target dictionary construction-based sparse representation hyperspectral target detection methods","volume":"12","author":"Zhu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1109\/TIP.2014.2323127","article-title":"Group-based sparse representation for image restoration","volume":"23","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","unstructured":"Tomasi, C., and Manduchi, R. (1998, January 7). Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271), Bombay, India."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TIP.2013.2287612","article-title":"Multispectral image denoising with optimized vector bilateral filter","volume":"23","author":"Peng","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3815","DOI":"10.1109\/TGRS.2014.2385082","article-title":"Spectral\u2013spatial kernel regularized for hyperspectral image denoising","volume":"53","author":"Yuan","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Peng, H., and Rao, R. (2010, January 26\u201329). Bilateral kernel parameter optimization by risk minimization. Proceedings of the 2010 IEEE International Conference on Image Processing, Hong Kong, China.","DOI":"10.1109\/ICIP.2010.5651045"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1109\/TGRS.2012.2205262","article-title":"Hyperion image optimization in coastal waters","volume":"51","author":"Zhao","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1002\/jmri.22003","article-title":"Adaptive non-local means denoising of MR images with spatially varying noise levels","volume":"31","author":"Collins","year":"2010","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TIP.2012.2210725","article-title":"Nonlocal transform-domain filter for volumetric data denoising and reconstruction","volume":"22","author":"Maggioni","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","unstructured":"Buades, A., Coll, B., and Morel, J.M. (2005, January 20\u201325). A non-local algorithm for image denoising. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_16","first-page":"442","article-title":"Hyperspectral image denoising using spatio-spectral total variation","volume":"13","author":"Aggarwal","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3192912","article-title":"Graph spatio-spectral total variation model for hyperspectral image denoising","volume":"19","author":"Takemoto","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. Nonlinear Phenom."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3050","DOI":"10.1109\/JSTARS.2015.2398433","article-title":"Hyperspectral image denoising via noise-adjusted iterative low-rank matrix approximation","volume":"8","author":"He","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2013.2284280","article-title":"Hyperspectral image restoration using low-rank matrix recovery","volume":"52","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TGRS.2008.918089","article-title":"Hyperspectral subspace identification","volume":"46","author":"Nascimento","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6196","DOI":"10.1109\/TGRS.2018.2833473","article-title":"Spatial\u2013spectral total variation regularized low-rank tensor decomposition for hyperspectral image denoising","volume":"56","author":"Fan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.neucom.2018.11.039","article-title":"Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation","volume":"330","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gr\u00fcnwald, P.D. (2007). The Minimum Description Length Principle, MIT Press.","DOI":"10.7551\/mitpress\/4643.001.0001"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1109\/36.934071","article-title":"Unsupervised target detection in hyperspectral images using projection pursuit","volume":"39","author":"Chiang","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1109\/36.885200","article-title":"Unsupervised hyperspectral image analysis with projection pursuit","volume":"38","author":"Ifarraguerri","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1109\/LGRS.2014.2311168","article-title":"Principal skewness analysis: Algorithm and its application for multispectral\/hyperspectral images indexing","volume":"11","author":"Geng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Richards, J.A. (2022). Remote Sensing Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-030-82327-6"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1109\/TCYB.2017.2677944","article-title":"Denoising hyperspectral image with non-iid noise structure","volume":"48","author":"Chen","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_31","first-page":"1","article-title":"Hyperspectral Image Denoising by Asymmetric Noise Modeling","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"702","article-title":"FastHyMix: Fast and parameter-free hyperspectral image mixed noise removal","volume":"34","author":"Zhuang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5512716","DOI":"10.1109\/TGRS.2023.3277832","article-title":"Hyperspectral Image Denoising Via Robust Subspace Estimation and Group Sparsity Constraint","volume":"61","author":"Fu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","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_35","unstructured":"Tukey, J.W. (1977). Exploratory Data Analysis, UMI."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ortega, J.M., and Rheinboldt, W.C. (2000). Iterative Solution of Nonlinear Equations in Several Variables, SIAM.","DOI":"10.1137\/1.9780898719468"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1002\/wics.199","article-title":"The Bayesian information criterion: Background, derivation, and applications","volume":"4","author":"Neath","year":"2012","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10438","DOI":"10.1109\/TGRS.2020.3046038","article-title":"Hyperspectral image denoising based on global and nonlocal low-rank factorizations","volume":"59","author":"Zhuang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/LGRS.2012.2193372","article-title":"A new approach to change detection in multispectral images by means of ERGAS index","volume":"10","author":"Renza","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/18.857802","article-title":"An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis","volume":"46","author":"Chang","year":"2000","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/276\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:43:47Z","timestamp":1760103827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/276"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020276"],"URL":"https:\/\/doi.org\/10.3390\/rs16020276","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,1,10]]}}}