{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:53:03Z","timestamp":1760147583593,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["62171404"],"award-info":[{"award-number":["62171404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms. The noise formulation existing in HSIs is relatively complex and would change in conjunction with different devices and imaging settings. For real applications, applying denoising approaches without accurate close-to-reality noise modeling before unmixing may not improve, but rather degrade the unmixing performance. This study proposes a robust hyperspectral unmixing method with practical learning-based hyperspectral image denoising. We formulated a close-to-reality noise model for hyperspectral data and provide a calibration approach for the noise parameters. On the basis of the calibrated noise model, synthetic data were generated and used for training a KST-based denoising network. The noisy hyperspectral data were firstly denoised by the trained denoising network and were then used to perform the unmixing process. A variety of unmixing algorithms can be integrated into our method to improve the accuracy of unmixing in noisy situations. In the experiments, several widely used unmixing algorithms were employed to verify the effect of the proposed method. The experimental results on both synthetic and real demonstrated that our proposed method can handle HSI data with various gain settings and helps to improve the unmixing performance effectively.<\/jats:p>","DOI":"10.3390\/rs15041058","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T05:37:46Z","timestamp":1676439466000},"page":"1058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Robust Hyperspectral Unmixing with Practical Learning-Based Hyperspectral Image Denoising"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6661-023X","authenticated-orcid":false,"given":"Risheng","family":"Huang","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaorun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyu","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoqun","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9633","DOI":"10.1109\/TGRS.2020.3045799","article-title":"Multitemporal hyperspectral images change detection based on joint unmixing and information coguidance strategy","volume":"59","author":"Guo","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","first-page":"708","article-title":"Sparse unmixing-based change detection for multitemporal hyperspectral images","volume":"9","author":"Iordache","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1109\/TGRS.2015.2505183","article-title":"Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images","volume":"54","author":"Liu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4282","DOI":"10.1109\/TGRS.2011.2144605","article-title":"Hyperspectral unmixing via L1\/2 sparsity-constrained nonnegative matrix factorization","volume":"49","author":"Qian","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/TGRS.2008.2002882","article-title":"Constrained nonnegative matrix factorization for hyperspectral unmixing","volume":"47","author":"Jia","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.1109\/TGRS.2012.2213825","article-title":"Manifold regularized sparse NMF for hyperspectral unmixing","volume":"51","author":"Lu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4414","DOI":"10.1109\/JSTARS.2022.3175257","article-title":"Hyperspectral unmixing based on nonnegative matrix factorization: A comprehensive review","volume":"15","author":"Feng","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huang, R., Li, X., and Zhao, L. (2017). Nonnegative matrix factorization with data-guided constraints for hyperspectral unmixing. Remote. Sens., 9.","DOI":"10.3390\/rs9101074"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6531","DOI":"10.1109\/TGRS.2016.2586110","article-title":"Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers","volume":"54","author":"Lei","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, C., Ma, Y., Mei, X., Liu, C., and Ma, J. (2016). Hyperspectral unmixing with robust collaborative sparse regression. Remote Sens., 8.","DOI":"10.3390\/rs8070588"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4267","DOI":"10.1109\/JSTARS.2016.2519498","article-title":"Sparsity-regularized robust non-negative matrix factorization for hyperspectral unmixing","volume":"9","author":"He","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4027","DOI":"10.1109\/TIP.2015.2456508","article-title":"Robust hyperspectral unmixing with correntropy-based metric","volume":"24","author":"Wang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","unstructured":"Ding, C., Zhou, D., He, X., and Zha, H. (2006, January 25). R1-PCA: Rotational invariant L1-norm principal component analysis for robust subspace factorization. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA."},{"key":"ref_16","unstructured":"Nie, F., Huang, H., Cai, X., and Ding, C.H. (2010, January 6). Efficient and robust feature selection via joint \u21132,1-norms minimization. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2708","DOI":"10.1016\/j.patcog.2012.01.003","article-title":"Robust classification using \u21132,1-norm based regression model","volume":"45","author":"Ren","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s00521-013-1371-5","article-title":"Robust non-negative matrix factorization via joint sparse and graph regularization for transfer learning","volume":"23","author":"Yang","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/TGRS.2016.2616161","article-title":"Robust Sparse Hyperspectral Unmixing With \u21132,1 Norm","volume":"55","author":"Ma","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kong, D., Ding, C., and Huang, H. (2011, January 24). Robust nonnegative matrix factorization using L21-norm. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow Scotland, UK.","DOI":"10.1145\/2063576.2063676"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1109\/TGRS.2020.2999936","article-title":"Correntropy-based spatial\u2013spectral robust sparsity-regularized hyperspectral unmixing","volume":"59","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104898","DOI":"10.1016\/j.knosys.2019.104898","article-title":"Cauchy sparse NMF with manifold regularization: A robust method for hyperspectral unmixing","volume":"184","author":"Wang","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8235","DOI":"10.1109\/TGRS.2019.2919166","article-title":"Spectral-spatial robust nonnegative matrix factorization for hyperspectral unmixing","volume":"57","author":"Huang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/TIP.2019.2928627","article-title":"Hyperspectral image denoising via matrix factorization and deep prior regularization","volume":"29","author":"Lin","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2018.2865197","article-title":"Hyperspectral image denoising employing a spatial\u2013spectral deep residual convolutional neural network","volume":"57","author":"Yuan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1109\/TGRS.2011.2110657","article-title":"Signal-dependent noise modeling and model parameter estimation in hyperspectral images","volume":"49","author":"Acito","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1109\/TNNLS.2020.2978756","article-title":"3-D quasi-recurrent neural network for hyperspectral image denoising","volume":"32","author":"Wei","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ye, M., Chen, H., Ji, C., Lei, L., and Qian, Y. (August, January 28). Spectral-spatial joint noise estimation for hyperspectral images. Proceedings of the IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898136"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, T., Fu, Y., and Li, C. (2021, January 10\u201317). Hyperspectral image denoising with realistic data. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00225"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2885","DOI":"10.1007\/s11263-022-01660-2","article-title":"Guided Hyperspectral Image Denoising with Realistic Data","volume":"130","author":"Zhang","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, H., Xu, Q., Liu, J., Liu, Y., and Wang, J. (2020, January 23\u201328). Practical deep raw image denoising on mobile devices. Proceedings of the European Conference on Computer Vision. Springer, Glasgow, UK.","DOI":"10.1007\/978-3-030-58539-6_1"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.nima.2019.02.056","article-title":"Accurate and informative analysis of positron annihilation lifetime spectra by using Markov Chain Monte-Carlo Bayesian inference method","volume":"928","author":"Gu","year":"2019","journal-title":"Nucl. Instruments Methods Phys. Res. Sect. A"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Arad, B., and Ben-Shahar, O. (2016, January 8\u201316). Sparse Recovery of Hyperspectral Signal from Natural RGB Images. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_2"},{"key":"ref_34","unstructured":"Charbonnier, P., Blanc-Feraud, L., Aubert, G., and Barlaud, M. (1994, January 13\u201316). Two deterministic half-quadratic regularization algorithms for computed imaging. Proceedings of the 1st International Conference on Image Processing, Austin, TX, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Barron, J.T. (2019, January 15\u201320). A general and adaptive robust loss function. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00446"},{"key":"ref_36","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_37","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":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/JSTARS.2018.2800701","article-title":"Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial\u2013Spectral Total Variation","volume":"11","author":"He","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Clark, R.N., Swayze, G.A., Gallagher, A.J., King, T.V., and Calvin, W.M. (1993). The US Geological Survey, Digital Spectral Library. Version 1 (0.2 to 3.0 um), U.S. Geological Survey Open-File Report.","DOI":"10.3133\/ofr93592"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:36:33Z","timestamp":1760121393000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,15]]},"references-count":39,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041058"],"URL":"https:\/\/doi.org\/10.3390\/rs15041058","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,15]]}}}