{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T18:11:28Z","timestamp":1761156688688,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T00:00:00Z","timestamp":1558569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701429"],"award-info":[{"award-number":["41701429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["CUG170625"],"award-info":[{"award-number":["CUG170625"]}]},{"name":"the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["KLIGIP-2017B08"],"award-info":[{"award-number":["KLIGIP-2017B08"]}]},{"name":"the Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences","award":["LSIT201716D"],"award-info":[{"award-number":["LSIT201716D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression. In a spatial regularized sparse unmixing model, spatial consideration acts as an important role and develops from local neighborhood pixels to global structures. However, incorporating spatial relationships will increase the computational complexity, and it is inevitable that some negative influences obtained by inaccurate estimated abundances\u2019 spatial correlations will reduce the accuracy of the algorithms. To obtain a more reliable and efficient spatial regularized sparse unmixing results, a joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote-sensing imagery sparse unmixing is proposed in this paper. In this work, local block grouping is first utilized to gather and classify abundant spatial information in local blocks, and noise-adjusted principal component analysis is used to compress these series of classified local blocks and select the most significant ones. Then the representative spatial correlations are drawn and replace the traditional spatial regularization in the spatial regularized sparse unmixing method. Compared with total variation-based and non-local means-based sparse unmixing algorithms, the proposed approach can yield comparable experimental results with three simulated hyperspectral data cubes and two real hyperspectral remote-sensing images.<\/jats:p>","DOI":"10.3390\/rs11101223","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T02:22:00Z","timestamp":1558664520000},"page":"1223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing"],"prefix":"10.3390","volume":"11","author":[{"given":"Ruyi","family":"Feng","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-5850","authenticated-orcid":false,"given":"Yanfei","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Hubei Province Engineering Research Center of Natural Resources Remote Sensing Monitoring, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,23]]},"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","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/JSTARS.2013.2267204","article-title":"Progress in hyperspectral remote sensing science and technology in China over the past three decades","volume":"7","author":"Tong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, Y., Cheng, J., Song, R., Peng, H., Chen, Q., and Chen, X. (2018). Hyperspectral unmixing with bandwise generalized bilinear model. Remote Sens., 10.","DOI":"10.3390\/rs10101600"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A general end-to-end two-dimensional CNN framework for hyperspectral image change detection","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Luo, H., Liu, C., Wu, C., and Guo, X. (2018). Urban change detection based on dempster-shafer theory for multi-temporal very high-resolution imagery. Remote Sens., 10.","DOI":"10.3390\/rs10070980"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Luo, H., Wang, L., Wu, C., and Zhang, L. (2018). An improved method for impervious surface mapping incorporating LiDAR data and high-resolution imagery at different acquisition times. Remote Sens., 10.","DOI":"10.20944\/preprints201806.0257.v1"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, J., Luo, B., Doute, S., and Chanussot, J. (2018). Exploration of planetary hyperspectral images with unsupervised spectral unmixing: A case study of planet Mars. Remote Sens., 10.","DOI":"10.3390\/rs10050737"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Marcello, J., Eugenio, F., Martin, J., and Marques, F. (2018). Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery. Remote Sens., 10.","DOI":"10.3390\/rs10081208"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L., and Zhou, H. (2018). Hyperspectral unmixing via low-rank representation with sparse consistency constraint and spectral library pruning. Remote Sens., 10.","DOI":"10.3390\/rs10020339"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/JSTARS.2017.2775567","article-title":"Sparse hyperspectral unmixing via heuristic lp-norm approach","volume":"11","author":"Salehani","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1109\/TGRS.2016.2520399","article-title":"Linear spatial spectral mixture model","volume":"54","author":"Shi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TGRS.2004.839806","article-title":"Does independent component analysis play a role in unmixing hyperspectral data?","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TGRS.2010.2068053","article-title":"An approach based on constrained nonnegative matrix factorization to unmix hyperspectral data","volume":"49","author":"Liu","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1109\/JSTARS.2018.2805779","article-title":"Group low-rank nonnegative matrix factorization with semantic regularizer for hyperspectral unmixing","volume":"11","author":"Wang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1109\/LGRS.2018.2823425","article-title":"Sparsity-constrained deep nonnegative matrix factorization for hyperspectral unmixing","volume":"15","author":"Fang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","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 Observ. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2744","DOI":"10.1109\/TGRS.2011.2174443","article-title":"A new minimum-volume enclosing algorithm for endmember identification and abundance estimation in hyperspectral data","volume":"50","author":"Hendrix","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1109\/JSTARS.2011.2181340","article-title":"Automated extraction of image-based endmember bundles for improved spectral unmixing","volume":"5","author":"Somers","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/LGRS.2017.2779477","article-title":"Spectral unmixing with multiple dictionaries","volume":"15","author":"Cohen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, X., Tong, X., Plaza, A., Zhong, Y., Xie, H., and Zhang, L. (2017). Joint sparse sub-pixel model with endmember variability for remotely sensed imagery. Remote Sens., 9.","DOI":"10.3390\/rs9010015"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5171","DOI":"10.1109\/TGRS.2016.2557340","article-title":"Semiblind hyperspectral unmixing in the presence of spectral library mismatches","volume":"54","author":"Fu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TSP.2015.2486746","article-title":"Hyperspectral unmixing with spectral variability using a perturbed linear mixing model","volume":"64","author":"Thouvenin","year":"2016","journal-title":"IEEE Trans. Signal. Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3890","DOI":"10.1109\/TIP.2016.2579259","article-title":"Blind hyperspectral unmixing using an entended linear mixing model to address spectral variability","volume":"25","author":"Drumetz","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1109\/TIP.2018.2878958","article-title":"An augmented linear mixing model to address spectral variability for hyperspectral unmixing","volume":"28","author":"Hong","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Drumetz, L., Meyer, T.R., Chanussot, J., Bertozzi, A.L., and Jutten, C. (2019). Hyperspectral image unmixing with endmember bundles and group sparsity inducing mixed norms. IEEE Trans. Image Process.","DOI":"10.1109\/TIP.2019.2897254"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/JSTSP.2018.2877497","article-title":"SULoRA: Subspace unmixing with low rank attribute embedding for hyperspectral data analysis","volume":"12","author":"Hong","year":"2018","journal-title":"IEEE J. Sel. Top. Signal. Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bioucas-Dias, J.M., and Figueiredo, M. (2010, January 14\u201316). Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing. Proceedings of the 2nd IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland.","DOI":"10.1109\/WHISPERS.2010.5594963"},{"key":"ref_29","unstructured":"Iordache, M.D. (2011). A Sparse Regression Approach to Hyperspectral Unmixing. [Ph.D. Thesis, School of Electrical and Computer Engineering]."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rizkinia, M., and Okuda, M. (2017). Joint local abundance sparse unmixing for hyperspectral images. Remote Sens., 9.","DOI":"10.3390\/rs9121224"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2013.11.014","article-title":"Structured sparse method for hyperspectral unmixing","volume":"88","author":"Zhu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4813","DOI":"10.1109\/TIT.2008.929920","article-title":"On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations","volume":"54","author":"Bruckstein","year":"2008","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_33","first-page":"969","article-title":"Sparsity and incoherence in compressive sampling","volume":"23","author":"Candes","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","unstructured":"Pati, Y.C., Rezaiifar, R., and Krishnaprasad, P. (1993, January 1\u20133). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1109\/TGRS.2010.2098413","article-title":"Sparse unmixing of hyperspectral data","volume":"49","author":"Iordache","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, C., Ma, Y., Mei, X., Fan, F., Huang, J., and Ma, J. (2017). Sparse unmixing of hyperspectral data with noise level estimation. Remote Sens., 9.","DOI":"10.3390\/rs9111166"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Feng, R., Wang, L., and Zhong, Y. (2018). Least angle regression-based constrained sparse unmixing of hyperspectral remote sensing imagery. Remote Sens., 10.","DOI":"10.3390\/rs10101546"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4484","DOI":"10.1109\/TGRS.2012.2191590","article-title":"Total variation spatial regularization for sparse hyperspectral unmixing","volume":"50","author":"Iordache","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1109\/JSTARS.2013.2280063","article-title":"Non-local sparse unmixing for hyperspectral remote sensing imagery","volume":"7","author":"Zhong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/TGRS.2013.2240001","article-title":"Collaborative sparse regression for hyperspectral unmixing","volume":"52","author":"Iordache","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4364","DOI":"10.1109\/TGRS.2013.2281589","article-title":"MUSIC-CSR: Hyperspectral unmixing via multiple signal classification and collaborative sparse regression","volume":"52","author":"Iordache","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.isprsjprs.2014.07.009","article-title":"Adaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery","volume":"97","author":"Feng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5791","DOI":"10.1109\/JSTARS.2016.2570947","article-title":"Adaptive spatial regularization sparse unmixing strategy based on joint MAP for hyperspectral remote sensing imagery","volume":"9","author":"Feng","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1109\/TGRS.2018.2797200","article-title":"Spectral-spatial weighted sparse regression for hyperspectral image unmixing","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, S., Huang, T., Zhao, X., Liu, G., and Cheng, Y. (2018). Double reweighted sparse regression and graph regularization for hyperspectral unmixing. Remote Sens., 10.","DOI":"10.3390\/rs10071046"},{"key":"ref_46","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. D Nonlinear Phenom."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/TGRS.2018.2872888","article-title":"Hyperspectral unmixing via total variation regularized nonnegative tensor factorization","volume":"57","author":"Xiong","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1109\/TGRS.2018.2862899","article-title":"Locality and structure regularized low rank representation for hyperspectral image classification","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/LGRS.2014.2367028","article-title":"An improved nonlocal sparse unmixing algorithm for hyperspectral imagery","volume":"12","author":"Feng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1109\/JSTARS.2017.2651063","article-title":"Centralized collaborative sparse unmixing for hyperspectral images","volume":"10","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1109\/JSEN.2009.2038546","article-title":"Applications of kalman filtering to single hyperspectral signature analysis","volume":"10","author":"Wang","year":"2010","journal-title":"IEEE Sens. J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5767","DOI":"10.1109\/TGRS.2018.2825457","article-title":"Spatial discontinuity-weighted sparse unmixing of hyperspectral images","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1090\/S0025-5718-09-02242-X","article-title":"Convergence of the linearized Bregman iteration for l1-norm minimization","volume":"78","author":"Cai","year":"2009","journal-title":"Math. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1137\/090746379","article-title":"Bregmanized nonlocal regularization for deconvolution and sparse reconstruction","volume":"3","author":"Zhang","year":"2010","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/36.54356","article-title":"Enhancement of high spectral resolution remote sensing data by a noise-adjusted principal components transform","volume":"28","author":"Lee","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/36.338369","article-title":"A fast way to compute the noise-adjusted principal components transform matrix","volume":"32","author":"Roger","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2387","DOI":"10.1109\/36.789637","article-title":"Interference and noise-adjusted principal components analysis","volume":"37","author":"Chang","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/LGRS.2006.888105","article-title":"Spatially coherent nonlinear dimensionality reduction and segmentation of hyperspectral images","volume":"4","author":"Mohan","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s12652-015-0285-8","article-title":"Optimal band selection for hyperspectral data with improved differential evolution","volume":"6","author":"Li","year":"2015","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1109\/TGRS.2017.2700322","article-title":"Deep feature fusion for VHR remote sensing scene classification","volume":"55","author":"Chaib","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s10586-016-0569-6","article-title":"Link the remote sensing big data to the image features via wavelet transformation","volume":"19","author":"Wang","year":"2016","journal-title":"Cluster Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1109\/LSP.2012.2217329","article-title":"Non-local Euclidean medians","volume":"19","author":"Chaudhury","year":"2012","journal-title":"IEEE Signal. Proc. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1109\/TCYB.2014.2341031","article-title":"Graph ensemble boosting for imbalanced noisy graph stream classification","volume":"45","author":"Pan","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/18756891.2015.1129579","article-title":"Locally weighted learning: How and when does it work in Bayesian networks?","volume":"8","author":"Wu","year":"2015","journal-title":"Int. J. Comput. Int. Sys."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1016\/j.eswa.2014.09.019","article-title":"Self-adaptive attribute weighting for Naive Bayes classification","volume":"42","author":"Wu","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1137\/040616024","article-title":"A review of image denoising algorithm, with a new one","volume":"4","author":"Buades","year":"2005","journal-title":"Multiscale Model. Sim."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1016\/j.patcog.2009.09.023","article-title":"Two-stage image denoising by principal component analysis with local pixel grouping","volume":"43","author":"Zhang","year":"2010","journal-title":"Pattern Recogn."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/BF01581204","article-title":"On the Douglas-Rechford splitting method and the proximal point algorithm for maximal monotone operators","volume":"55","author":"Eckstein","year":"1992","journal-title":"Math. Program."},{"key":"ref_69","unstructured":"Jimenez, L.I., Martin, G., and Plaza, A. (2012, January 7\u20139). A new tool for evaluating spectral unmixing applications for remotely sensed hyperspectral image analysis. Proceedings of the International Conference Geographic Object-Based Image Analysis (GEOBIA), Rio de Janeiro, Brazil."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2016.04.008","article-title":"Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery","volume":"119","author":"Zhong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1223\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:32Z","timestamp":1760187272000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,23]]},"references-count":70,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11101223"],"URL":"https:\/\/doi.org\/10.3390\/rs11101223","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,5,23]]}}}