{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:58:17Z","timestamp":1760245097936,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The development of the hyperspectral remote sensor technology allows the acquisition of images with a very detailed spectral information for each pixel. Because of this, hyperspectral images (HSI) potentially possess larger capabilities in solving many scientific and practical problems in agriculture, biomedical, ecological, geological, hydrological studies. However, their analysis requires developing specialized and fast algorithms for data processing, due the high dimensionality of the data. In this work, we propose a new semi-supervised method for multilabel segmentation of HSI that combines a suitable linear discriminant analysis, a similarity index to compare different spectra, and a random walk based model with a direct label assignment. The user-marked regions are used for the projection of the original high-dimensional feature space to a lower dimensional space, such that the class separation is maximized. This allows to retain in an automatic way the most informative features, lightening the successive computational burden. The part of the random walk is related to a combinatorial Dirichlet problem involving a weighted graph, where the nodes are the projected pixel of the original HSI, and the positive weights depend on the distances between these nodes. We then assign to each pixel of the original image a probability quantifying the likelihood that the pixel (node) belongs to some subregion. The computation of the spectral distance involves both the coordinates in a features space of a pixel and of its neighbors. The final segmentation process is therefore reduced to a suitable optimization problem coupling the probabilities from the random walker computation, and the similarity with respect the initially labeled pixels. We discuss the properties of the new method with experimental results carried on benchmark images.<\/jats:p>","DOI":"10.3390\/jimaging7120267","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T11:00:23Z","timestamp":1638874823000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Semi-Supervised Reduced-Space Method for Hyperspectral Imaging Segmentation"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7466-2969","authenticated-orcid":false,"given":"Giacomo","family":"Aletti","sequence":"first","affiliation":[{"name":"Environmental Science and Policy Department, Universit\u00e0 degli Studi di Milano, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2985-374X","authenticated-orcid":false,"given":"Alessandro","family":"Benfenati","sequence":"additional","affiliation":[{"name":"Environmental Science and Policy Department, Universit\u00e0 degli Studi di Milano, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9191-6003","authenticated-orcid":false,"given":"Giovanni","family":"Naldi","sequence":"additional","affiliation":[{"name":"Environmental Science and Policy Department, Universit\u00e0 degli Studi di Milano, 20133 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Elmasry, G., Mandour, N., Al-Rejaie, S., Belin, E., and Rousseau, D. (2019). Recent applications of multispectral imaging in seed phenotyping and quality monitoring\u2014An overview. Sensors, 19.","DOI":"10.3390\/s19051090"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1146\/annurev-food-032818-121155","article-title":"Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications","volume":"10","author":"Ma","year":"2019","journal-title":"Annu. Rev. Food Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced Spectral Classifiers for Hyperspectral Images: A review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MGRS.2020.2979764","article-title":"Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep: Overview and Toolbox","volume":"8","author":"Rasti","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Guti\u00e9rrez-Guti\u00e9rrez, J., Pardo, A., Real, E., L\u00f3pez-Higuera, J., and Conde, O. (2019). Custom scanning hyperspectral imaging system for biomedical applications: Modeling, benchmarking, and specifications. Sensors, 19.","DOI":"10.3390\/s19071692"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Halicek, M., Fabelo, H., Ortega, S., Callico, G., and Fei, B. (2019). In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: Revealing the invisible features of cancer. Cancers, 11.","DOI":"10.3390\/cancers11060756"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.patcog.2019.01.026","article-title":"Hyperspectral document image processing: Applications, challenges and future prospects","volume":"90","author":"Qureshi","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1002\/eap.1682","article-title":"Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems","volume":"28","author":"Hestir","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.marpolbul.2017.11.011","article-title":"Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics","volume":"126","author":"Peters","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MGRS.2016.2637824","article-title":"Hyperspectral and multispectral data fusion: A comparative review of the recent literature","volume":"5","author":"Yokoya","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/MGRS.2018.2793873","article-title":"Discriminant Analysis-Based Dimension Reduction for Hyperspectral Image Classification: A Survey of the Most Recent Advances and an Experimental Comparison of Different Techniques","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Signoroni, A., Savardi, M., Baronio, A., and Benini, S. (2019). Deep learning meets hyperspectral image analysis: A multidisciplinary review. J. Imaging, 5.","DOI":"10.3390\/jimaging5050052"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","article-title":"Modern Trends in Hyperspectral Image Analysis: A Review","volume":"6","author":"Khan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rasti, B., Scheunders, P., Ghamisi, P., Licciardi, G., and Chanussot, J. (2018). Noise reduction in hyperspectral imagery: Overview and application. Remote Sens., 10.","DOI":"10.3390\/rs10030482"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep learning for hyperspectral image classification: An overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep learning classifiers for hyperspectral imaging: A review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","first-page":"3","article-title":"Classification and Segmentation Models for Hyperspectral Imaging\u2014An Overview","volume":"1382","author":"Shah","year":"2021","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6547","DOI":"10.1109\/TGRS.2017.2729882","article-title":"Multiple Kernel Learning for Hyperspectral Image Classification: A Review","volume":"55","author":"Gu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"582","DOI":"10.3103\/S8756699018060079","article-title":"Spectral-Spatial Methods for Hyperspectral Image Classification. Review","volume":"54","author":"Borzov","year":"2018","journal-title":"Optoelectron. Instrum. Data Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Le Moan, S., and Cariou, C. (2020). Minimax bridgeness-based clustering for hyperspectral data. Remote Sens., 12.","DOI":"10.3390\/rs12071162"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6712","DOI":"10.1109\/TGRS.2018.2841823","article-title":"Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bonettini, S., Benfenati, A., and Ruggiero, V. (2014, January 27\u201330). Primal-dual first order methods for total variation image restoration in presence of poisson noise. Proceedings of the 2014 IEEE International Conference on Image Processing, ICIP 2014, Paris, France.","DOI":"10.1109\/ICIP.2014.7025844"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1137\/14097642X","article-title":"Scaling techniques for \u03f5-subgradient methods","volume":"26","author":"Bonettini","year":"2016","journal-title":"SIAM J. Optim."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/TGRS.2004.837327","article-title":"Multispectral land sensing: Where from, where to?","volume":"43","author":"Landgrebe","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF00048682","article-title":"Multinomial logistic regression algorithm","volume":"44","year":"1992","journal-title":"Ann. Inst. Stat. Math."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TGRS.2011.2162649","article-title":"Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1080\/01431169308904316","article-title":"Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data","volume":"14","author":"Benediktsson","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1080\/014311699213622","article-title":"A back-propagation neural network for mineralogical mapping from AVIRIS data","volume":"20","author":"Yang","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1109\/TGRS.2019.2893180","article-title":"Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification","volume":"57","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/1687-6180-2014-71","article-title":"Classification of hyperspectral imagery with neural networks: Comparison to conventional tools","volume":"2014","author":"Farrand","year":"2014","journal-title":"Eurasip J. Adv. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1109\/TGRS.2007.892009","article-title":"Use of neural networks for automatic classification from high-resolution images","volume":"45","author":"Pacifici","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/JSTARS.2014.2359965","article-title":"Extreme Learning Machine with Composite Kernels for Hyperspectral Image Classification","volume":"8","author":"Zhou","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/JSTARS.2013.2251969","article-title":"Combining multiple classification methods for hyperspectral data interpretation","volume":"6","author":"Santos","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1016\/j.future.2003.11.011","article-title":"Assessment of the effectiveness of support vector machines for hyperspectral data","volume":"20","author":"Pal","year":"2004","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1109\/TGRS.2012.2230268","article-title":"Generalized composite kernel framework for hyperspectral image classification","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/36.752215","article-title":"Maximizing land cover classification accuracies produced by decision trees at continental to global scales","volume":"37","author":"Friedl","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An assessment of the effectiveness of decision tree methods for land cover classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1109\/JSTARS.2016.2636877","article-title":"Hyperspectral Image Classification with Rotation Random Forest via KPCA","volume":"10","author":"Xia","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","article-title":"Hyperspectral image classification using dictionary-based sparse representation","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1137\/110837486","article-title":"Dictionary learning for noisy and incomplete hyperspectral images","volume":"5","author":"Xing","year":"2012","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_43","unstructured":"Zeng, H., Liu, Q., Zhang, M., Han, X., and Wang, Y. (2020). Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/LGRS.2018.2869563","article-title":"Spectral\u2013Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification","volume":"16","author":"Qin","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hanachi, R., Sellami, A., Farah, I.R., and Mura, M.D. (2021, January 22\u201323). Semi-supervised Classification of Hyperspectral Image through Deep Encoder-Decoder and Graph Neural Networks. Proceedings of the 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Istanbul, Turkey.","DOI":"10.1109\/ICOTEN52080.2021.9493562"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Aletti, G., Benfenati, A., and Naldi, G. (2021). A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances. J. Imaging, 7.","DOI":"10.3390\/jimaging7100208"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1016\/j.jcss.2007.08.006","article-title":"Towards a theoretical foundation for Laplacian-based manifold methods","volume":"74","author":"Belkin","year":"2008","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"012011","DOI":"10.1088\/1742-6596\/657\/1\/012011","article-title":"Image regularization for Poisson data","volume":"657","author":"Benfenati","year":"2015","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bertero, M., Boccacci, P., and Ruggiero, V. (2018). Inverse Imaging with Poisson Data, IOP Publishing.","DOI":"10.1088\/2053-2563\/aae109"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.cnsns.2014.06.045","article-title":"Inexact Bregman iteration for deconvolution of superimposed extended and point sources","volume":"20","author":"Benfenati","year":"2015","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1109\/TPAMI.2006.233","article-title":"Random walks for image segmentation","volume":"28","author":"Grady","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TIP.2016.2621663","article-title":"Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation","volume":"26","author":"Bampis","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.1109\/TPAMI.2020.2974475","article-title":"Laplacian Coordinates: Theory and Methods for Seeded Image Segmentation","volume":"43","author":"Casaca","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.jmva.2007.08.001","article-title":"An extension of Fisher\u2019s discriminant analysis for stochastic processes","volume":"99","author":"Shin","year":"2008","journal-title":"J. Multivar. Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TGRS.2008.2005729","article-title":"Classification of hyperspectral images with regularized linear discriminant analysis","volume":"47","author":"Bandos","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ye, J., Xiong, T., Li, Q., Janardan, R., Bi, J., Cherkassky, V., and Kambhamettu, C. (2006, January 6\u201311). Efficient model selection for regularized linear discriminant analysis. Proceedings of the International Conference on Information and Knowledge Management, Arlington, VA, USA.","DOI":"10.1145\/1183614.1183691"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1137\/17M1141977","article-title":"Efficient randomized algorithms for the fixed-precision low-rank matrix approximation","volume":"39","author":"Yu","year":"2018","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Cagli, E., Carrera, D., Aletti, G., Naldi, G., and Rossi, B. (2013, January 20\u201323). Robust DOA estimation of speech signals via sparsity models using microphone arrays. Proceedings of the 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA.","DOI":"10.1109\/WASPAA.2013.6701823"},{"key":"ref_59","unstructured":"Aletti, G., Naldi, G., and Parigi, G. (2016, January 13\u201317). Around the image analysis of the vessels remodelling during embryos development. Proceedings of the 19th European Conference on Mathematics for Industry, Santiago de Compostela, Spain."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s10440-019-00281-1","article-title":"A new nonlocal nonlinear diffusion equation for data analysis","volume":"168","author":"Aletti","year":"2020","journal-title":"Acta Appl. Math."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhu, C., Bichot, C.E., and Masnou, S. (2013, January 15\u201318). Graph-based image segmentation using weighted color patch. Proceedings of the 2013 IEEE International Conference on Image Processing, Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738837"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1142\/S0218202518500276","article-title":"An analytical framework for consensus-based global optimization method","volume":"28","author":"Carrillo","year":"2018","journal-title":"Math. Model. Methods Appl. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1016\/j.aml.2013.04.007","article-title":"Nonlinear microscale interactions in the kinetic theory of active particles","volume":"26","author":"Benfenati","year":"2013","journal-title":"Appl. Math. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2010). Particle Swarm Optimization. Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Grady, L., and Polimeni, J.R. (2010). Discrete Calculus: Applied Analysis on Graphs for Computational Science, Springer.","DOI":"10.1007\/978-1-84996-290-2"},{"key":"ref_66","unstructured":"Bezdek, J.C. (2013). Pattern Recognition with Fuzzy Objective Function Algorithms, Springer Science & Business Media."},{"key":"ref_67","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_68","doi-asserted-by":"crossref","unstructured":"Cariou, C., and Chehdi, K. (2016, January 10\u201315). A new k-nearest neighbor density-based clustering method and its application to hyperspectral images. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730609"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.media.2015.06.012","article-title":"Multi-atlas segmentation of biomedical images: A survey","volume":"24","author":"Iglesias","year":"2015","journal-title":"Med. Image Anal."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/12\/267\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:42:19Z","timestamp":1760168539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/12\/267"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,7]]},"references-count":69,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["jimaging7120267"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7120267","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2021,12,7]]}}}