{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:25:32Z","timestamp":1761719132509,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"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":["62071084"],"award-info":[{"award-number":["62071084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds in Heilongjiang Provincial Universities","award":["145109218"],"award-info":[{"award-number":["145109218"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In hyperspectral remote sensing, the clustering technique is an important issue of concern. Affinity propagation is a widely used clustering algorithm. However, the complex structure of the hyperspectral image (HSI) dataset presents challenge for the application of affinity propagation. In this paper, an improved version of affinity propagation based on complex wavelet structural similarity index and local outlier factor is proposed specifically for the HSI dataset. In the proposed algorithm, the complex wavelet structural similarity index is used to calculate the spatial similarity of HSI pixels. Meanwhile, the calculation strategy of the spatial similarity is simplified to reduce the computational complexity. The spatial similarity and the traditional spectral similarity of the HSI pixels jointly constitute the similarity matrix of affinity propagation. Furthermore, the local outlier factors are applied as weights to revise the original exemplar preferences of the affinity propagation. Finally, the modified similarity matrix and exemplar preferences are applied, and the clustering index is obtained by the traditional affinity propagation. Extensive experiments were conducted on three HSI datasets, and the results demonstrate that the proposed method can improve the performance of the traditional affinity propagation and provide competitive clustering results among the competitors.<\/jats:p>","DOI":"10.3390\/rs14051195","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T20:11:57Z","timestamp":1646079117000},"page":"1195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Affinity Propagation Based on Structural Similarity Index and Local Outlier Factor for Hyperspectral Image Clustering"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6652-3611","authenticated-orcid":false,"given":"Haimiao","family":"Ge","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150000, China"}]},{"given":"Haizhu","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Yuexia","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Xiaoyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Moqi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Ou, D.P., Tan, K., Du, Q., Zhu, J.S., Wang, X., and Chen, Y. (2019). A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction. Remote Sens., 11.","key":"ref_1","DOI":"10.3390\/rs11060654"},{"doi-asserted-by":"crossref","unstructured":"Chung, B., Yu, J., Wang, L., Kim, N.H., Lee, B.H., Koh, S., and Lee, S. (2020). Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing-A Laboratory Approach. Remote Sens., 12.","key":"ref_2","DOI":"10.3390\/rs12081325"},{"key":"ref_3","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. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"083567","DOI":"10.1117\/1.JRS.8.083567","article-title":"Pixel classification of large-size hyperspectral images by affinity propagation","volume":"8","author":"Chehdi","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/MGRS.2020.3032575","article-title":"Hyperspectral Image Clustering: Current Achievements and Future Lines","volume":"9","author":"Zhai","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TPAMI.2002.1017616","article-title":"An efficient k-means clustering algorithm: Analysis and implementation","volume":"24","author":"Kanungo","year":"2002","journal-title":"Ieee Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","first-page":"100","article-title":"Algorithm AS 136: A K-Means Clustering Algorithm","volume":"28","author":"Wong","year":"1979","journal-title":"J. R. Stat. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.eswa.2016.03.008","article-title":"DENDIS: A new density-based sampling for clustering algorithm","volume":"56","author":"Ros","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.ins.2021.08.036","article-title":"Density peak clustering using global and local consistency adjustable manifold distance","volume":"577","author":"Tao","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"Rodriguez","year":"2014","journal-title":"Science"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1198\/016214502760047131","article-title":"Model-based clustering, discriminant analysis, and density estimation","volume":"97","author":"Fraley","year":"2002","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1006\/dspr.1999.0361","article-title":"Speaker verification using adapted Gaussian mixture models","volume":"10","author":"Reynolds","year":"2000","journal-title":"Digit. Signal. Processing"},{"doi-asserted-by":"crossref","unstructured":"Fakoor, D., Maihami, V., and Maihami, R. (2021). A machine learning recommender system based on collaborative filtering using Gaussian mixture model clustering. Mathematucal Methods in the Applied Science, Wiley Online Library.","key":"ref_14","DOI":"10.1002\/mma.7801"},{"doi-asserted-by":"crossref","unstructured":"Fuchs, R., Pommeret, D., and Viroli, C. (2021). Mixed Deep Gaussian Mixture Model: A clustering model for mixed datasets. Adv. Data Anal. Classif., 1\u201323.","key":"ref_15","DOI":"10.1007\/s11634-021-00466-3"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4524","DOI":"10.1109\/TGRS.2013.2282356","article-title":"An unsupervised spectral matching classifier based on artificial DNA computing for hyperspectral remote sensing imagery","volume":"52","author":"Jiao","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"4202","DOI":"10.1109\/TGRS.2015.2393357","article-title":"Adaptive multiobjective memetic fuzzy clustering algorithm for remote sensing imagery","volume":"53","author":"Ma","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2786","DOI":"10.1109\/TGRS.2017.2654486","article-title":"Unsupervised classification in hyperspectral imagery with nonlocal total variation and primal-dual hybrid gradient algorithm","volume":"55","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s00530-015-0450-0","article-title":"Spectral\u2013spatial co-clustering of hyperspectral image data based on bipartite graph","volume":"22","author":"Liu","year":"2016","journal-title":"Multimed. Syst."},{"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.","key":"ref_22","DOI":"10.3390\/rs9040335"},{"key":"ref_23","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."},{"doi-asserted-by":"crossref","unstructured":"Tian, L., Du, Q., Kopriva, I., and Younan, N. (2018, January 22\u201327). Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","key":"ref_24","DOI":"10.1109\/IGARSS.2018.8519284"},{"doi-asserted-by":"crossref","unstructured":"Shahi, K.R., Khodadadzadeh, M., Tusa, L., Ghamisi, P., Tolosana-Delgado, R., and Gloaguen, R. (2020). Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis. Remote Sens., 12.","key":"ref_25","DOI":"10.3390\/rs12152421"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/TMM.2017.2745702","article-title":"Cnn-based joint clustering and representation learning with feature drift compensation for large-scale image data","volume":"20","author":"Hsu","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_27","first-page":"3861","article-title":"Towards k-means-friendly spaces: Simultaneous deep learning and clustering","volume":"Volume 70","author":"Yang","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.ins.2021.07.003","article-title":"Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering","volume":"578","author":"Cai","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1109\/LGRS.2017.2786732","article-title":"Unsupervised hyperspectral remote sensing image clustering based on adaptive density","volume":"15","author":"Xie","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"doi-asserted-by":"crossref","unstructured":"Neagoe, V.-E., and Chirila-Berbentea, V. (2016, January 10\u201315). Improved Gaussian mixture model with expectation-maximization for clustering of remote sensing imagery. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","key":"ref_30","DOI":"10.1109\/IGARSS.2016.7729792"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing messages between data points","volume":"315","author":"Frey","year":"2007","journal-title":"Science"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4719","DOI":"10.1007\/s11042-020-09822-5","article-title":"Gabor face clustering using affinity propagation and structural similarity index","volume":"80","author":"Dagher","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6391","DOI":"10.1080\/01431161.2021.1934595","article-title":"A semi-supervised learning method for hyperspectral imagery based on self-training and local-based affinity propagation","volume":"42","author":"Ge","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"094012","DOI":"10.1088\/1361-6501\/abfef5","article-title":"Intelligent fault diagnosis for rotating machinery based on potential energy feature and adaptive transfer affinity propagation clustering","volume":"32","author":"Li","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.saa.2017.12.074","article-title":"Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy","volume":"194","author":"Liu","year":"2018","journal-title":"Spectrochim. Acta Part A-Mol. Biomol. Spectrosc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103321","DOI":"10.1016\/j.jvcir.2021.103321","article-title":"Video summary generation by visual shielding compressed sensing coding and double-layer affinity propagation","volume":"81","author":"Liu","year":"2021","journal-title":"J. Vis. Commun. Image Represent."},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y.J., Deng, J., Zhu, K.K., Tao, Y.Q., Liu, X.L., and Cui, L.G. (2021). Location and Expansion of Electric Bus Charging Stations Based on Gridded Affinity Propagation Clustering and a Sequential Expansion Rule. Sustainability, 13.","key":"ref_37","DOI":"10.3390\/su13168957"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9915315","DOI":"10.1155\/2021\/9915315","article-title":"Multivariate Time Series Data Clustering Method Based on Dynamic Time Warping and Affinity Propagation","volume":"2021","author":"Wan","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_39","first-page":"425","article-title":"Aaptive semi-supervised affinity propagation clustering algorithm based on structural similarity","volume":"23","author":"Wang","year":"2016","journal-title":"Teh. Vjesn.-Tech. Gaz."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/LSP.2017.2674960","article-title":"Unsupervized Image Clustering With SIFT-Based Soft-Matching Affinity Propagation","volume":"24","author":"Zhang","year":"2017","journal-title":"Ieee Signal. Processing Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/JSTARS.2020.3040218","article-title":"Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering","volume":"14","author":"Qin","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Fan, L., and Messinger, D.W. (2018). Joint spatial-spectral hyperspectral image clustering using block-diagonal amplified affinity matrix. Opt. Eng., 57.","key":"ref_42","DOI":"10.1117\/1.OE.57.3.033107"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1007\/s00521-014-1671-4","article-title":"Stability-based preference selection in affinity propagation","volume":"25","author":"Chen","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1016\/j.patcog.2014.11.003","article-title":"Subspace clustering using affinity propagation","volume":"48","author":"Gan","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.patrec.2016.11.017","article-title":"Adjustable preference affinity propagation clustering","volume":"85","author":"Li","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/tee.22792","article-title":"Adaptive Affinity Propagation Algorithm Based on New Strategy of Dynamic Damping Factor and Preference","volume":"14","author":"Hu","year":"2019","journal-title":"Ieej Trans. Electr. Electron. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"Ieee Trans. Image Processing"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1109\/TIP.2009.2025923","article-title":"Complex wavelet structural similarity: A new image similarity index","volume":"18","author":"Sampat","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1016\/j.image.2012.07.004","article-title":"Image classification based on complex wavelet structural similarity","volume":"28","author":"Rehman","year":"2013","journal-title":"Signal. Processing-Image Commun."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1080\/17686733.2019.1579020","article-title":"Making long-wave infrared face recognition robust against image quality degradations","volume":"16","author":"Bovik","year":"2019","journal-title":"Quant. Infrared Thermogr. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1109\/JSTARS.2013.2282161","article-title":"A Two-Stage Feature Selection Framework for Hyperspectral Image Classification Using Few Labeled Samples","volume":"7","author":"Jia","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1109\/TMI.2014.2365436","article-title":"Analysis of Structural Similarity in Mammograms for Detection of Bilateral Asymmetry","volume":"34","author":"Casti","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1145\/335191.335388","article-title":"LOF: Identifying density-based local outliers","volume":"29","author":"Breunig","year":"2000","journal-title":"Sigmod Rec."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1109\/LGRS.2018.2842792","article-title":"Hyperspectral Imagery Noisy Label Detection by Spectral Angle Local Outlier Factor","volume":"15","author":"Tu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1080\/2150704X.2016.1266408","article-title":"Detection and mitigation of radiometers radio-frequency interference by using the local outlier factor","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/LGRS.2020.2994745","article-title":"Hyperspectral Anomaly Detection Based on Low-Rank Representation Using Local Outlier Factor","volume":"18","author":"Yu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"042604","DOI":"10.1117\/1.JRS.15.042604","article-title":"Self-training algorithm for hyperspectral imagery classification based on mixed measurement k-nearest neighbor and support vector machine","volume":"15","author":"Ge","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1109\/TIFS.2012.2189206","article-title":"Feature Band Selection for Online Multispectral Palmprint Recognition","volume":"7","author":"Guo","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1023\/A:1026553619983","article-title":"A parametric texture model based on joint statistics of complex wavelet coefficients","volume":"40","author":"Portilla","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/18.119725","article-title":"Shiftable multiscale transforms","volume":"38","author":"Simoncelli","year":"1992","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112012","DOI":"10.1016\/j.rse.2020.112012","article-title":"WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H-2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF","volume":"250","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","year":"2007","journal-title":"Stat. Comput."},{"key":"ref_63","first-page":"659","article-title":"Gaussian mixture models","volume":"741","author":"Reynolds","year":"2009","journal-title":"Encycl. Biom."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","article-title":"ST-DBSCAN: An algorithm for clustering spatial\u2013temporal data","volume":"60","author":"Birant","year":"2007","journal-title":"Data Knowl. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.knosys.2016.02.001","article-title":"Study on density peaks clustering based on k-nearest neighbors and principal component analysis","volume":"99","author":"Du","year":"2016","journal-title":"Knowl.-Based Syst."},{"unstructured":"Kohonen, T. (1997, January 12). Exploration of very large databases by self-organizing maps. Proceedings of the International Conference on Neural Networks (icnn\u201997), Houston, TX, USA.","key":"ref_66"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1016\/j.neucom.2010.11.028","article-title":"Integrating feature maps and competitive layer architectures for motion segmentation","volume":"74","author":"Steffen","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/S0031-3203(98)00091-0","article-title":"An overlap invariant entropy measure of 3D medical image alignment","volume":"32","author":"Studholme","year":"1999","journal-title":"Pattern Recognit."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1109\/TNNLS.2013.2293795","article-title":"Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation","volume":"25","author":"Huang","year":"2014","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1195\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:29:37Z","timestamp":1760135377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1195"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"references-count":69,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051195"],"URL":"https:\/\/doi.org\/10.3390\/rs14051195","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,2,28]]}}}