{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:24:55Z","timestamp":1772828695134,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013609","name":"EEA Grants\/Norway Grants","doi-asserted-by":"publisher","award":["24\/2020"],"award-info":[{"award-number":["24\/2020"]}],"id":[{"id":"10.13039\/501100013609","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"publisher","award":["270959"],"award-info":[{"award-number":["270959"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"publisher","award":["223254"],"award-info":[{"award-number":["223254"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral remote sensing reveals detailed information about the optical response of a scene. Self-Organizing Maps (SOMs) can partition a hyperspectral dataset into clusters, both to enable more analysis on-board the imaging platform and to reduce downlink time. Here, the expected on-board performance of the SOM algorithm is calculated within two different satellite operational procedures: one in which the SOM is trained prior to imaging, and another in which the training is part of the operations. The two procedures are found to have advantages that are suitable to quite different situations. The computational requirements for SOMs of different sizes are benchmarked on the target hardware for the HYPSO-1 mission, and dimensionality reduction (DR) is tested as a way of reducing the SOM network size. We find that SOMs can run on the target on-board processing hardware, can be trained reasonably well using less than 0.1% of the total pixels in a scene, are accelerated by DR, and can achieve a relative quantization error of about 1% on scenes acquired by a previous hyperspectral imaging satellite, HICO. Moreover, if class labels are assigned to the nodes of the SOM, these networks can classify with a comparable accuracy to support vector machines, a common benchmark, on a few simple scenes.<\/jats:p>","DOI":"10.3390\/rs13204174","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"4174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Self-Organizing Maps for Clustering Hyperspectral Images On-Board a CubeSat"],"prefix":"10.3390","volume":"13","author":[{"given":"Aksel S.","family":"Danielsen","sequence":"first","affiliation":[{"name":"Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491 Trondheim, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9440-5989","authenticated-orcid":false,"given":"Tor Arne","family":"Johansen","sequence":"additional","affiliation":[{"name":"Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491 Trondheim, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8265-0661","authenticated-orcid":false,"given":"Joseph L.","family":"Garrett","sequence":"additional","affiliation":[{"name":"Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491 Trondheim, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1364\/AO.50.001501","article-title":"Hyperspectral Imager for the Coastal Ocean: Instrument description and first images","volume":"50","author":"Lucke","year":"2011","journal-title":"Appl. Opt."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/JSTARS.2013.2249496","article-title":"The Earth Observing One (EO-1) Satellite Mission: Over a Decade in Space","volume":"6","author":"Middleton","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, T., Liu, K., Li, Y., and Zhang, L. (2016). Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification. Remote Sens., 8.","DOI":"10.3390\/rs8050438"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Transon, J., D\u2019Andrimont, R., Maugnard, A., and Defourny, P. (2018). Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. Remote Sens., 10.","DOI":"10.3390\/rs10020157"},{"key":"ref_5","unstructured":"Lopinto, E., and Ananasso, C. (2020, January 3\u20136). The Prisma Hyperspectral Mission. Proceedings of the 33rd EARSeL Symposium, Matera, Italy."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Matsunaga, T., Iwasaki, A., Tachikawa, T., Tanii, J., Kashimura, O., Mouri, K., Inada, H., Tsuchida, S., Nakamura, R., and Yamamoto, H. (2020, January 2\u20134). Hyperspectral Imager Suite (HISUI): Its Launch and Current Status. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, Gujarat, India.","DOI":"10.1109\/IGARSS39084.2020.9323376"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Toorian, A., Diaz, K., and Lee, S. (2008, January 1\u20138). The CubeSat Approach to Space Access. Proceedings of the 2008 IEEE Aerospace Conference, Big Sky, MT, USA. ISSN: 1095-323X.","DOI":"10.1109\/AERO.2008.4526293"},{"key":"ref_9","unstructured":"Karafolas, N., Sodnik, Z., and Cugny, B. (2018, January 9\u201312). In-orbit demonstration of the first hyperspectral imager for nanosatellites. Proceedings of the International Conference on Space Optics\u2014ICSO 2018, Chania, Greece."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.actaastro.2020.11.044","article-title":"Aalto-1, multi-payload CubeSat: In-orbit results and lessons learned","volume":"187","author":"Mughal","year":"2021","journal-title":"Acta Astronaut."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gr\u00f8tte, M.E., Birkeland, R., Honor\u00e9-Livermore, E., Bakken, S., Garrett, J.L., Prentice, E.F., Sigernes, F., Orlandi\u0107, M., Gravdahl, J.T., and Johansen, T.A. (2021). Ocean Color Hyperspectral Remote Sensing With High Resolution and Low Latency\u2014The HYPSO-1 CubeSat Mission. IEEE Trans. Geosci. Remote Sens., 1\u201319.","DOI":"10.1109\/TGRS.2021.3080175"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dallolio, A., Quintana-Diaz, G., Honor\u00e9-Livermore, E., Garrett, J.L., Birkeland, R., and Johansen, T.A. (2021). A Satellite-USV System for Persistent Observation of Mesoscale Oceanographic Phenomena. Remote Sens., 13.","DOI":"10.3390\/rs13163229"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Orlandi\u0107, M., Fjeldtvedt, J., and Johansen, T.A. (2019). A Parallel FPGA Implementation of the CCSDS-123 Compression Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11060673"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: A review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Comput. Surv."},{"key":"ref_15","unstructured":"Bishop, C.M. (2006). Pattern Rec3ognition and Machine Learning, Springer. Information Science and Statistics."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ranjan, S., Nayak, D.R., Kumar, K.S., Dash, R., and Majhi, B. (2017, January 6\u20137). Hyperspectral image classification: A k-means clustering based approach. Proceedings of the 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS.2017.8014707"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ismail, M., and Orlandi\u0107, M. (2020). Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures. Algorithms, 13.","DOI":"10.3390\/a13120330"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neunet.2012.09.018","article-title":"Essentials of the self-organizing map","volume":"37","author":"Kohonen","year":"2013","journal-title":"Neural Netw."},{"key":"ref_20","unstructured":"Farka\u0161, I., Masulli, P., and Wermter, S. (2020). A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models. Artificial Neural Networks and Machine Learning, Springer International Publishing. Lecture Notes in Computer Science."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/BF00337288","article-title":"Self-organized formation of topologically correct feature maps","volume":"43","author":"Kohonen","year":"1982","journal-title":"Biol. Cybern."},{"key":"ref_22","first-page":"1","article-title":"Bibliography of self-organizing map (SOM) papers: 1981\u20131997","volume":"1","author":"Kaski","year":"1998","journal-title":"Neural Comput. Surv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/72.668891","article-title":"Image compression by self-organized Kohonen map","volume":"9","author":"Amerijckx","year":"1998","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_24","unstructured":"Hecht, T., Lefort, M., and Gepperth, A. (2015, January 22\u201324). Using self-organizing maps for regression: The importance of the output function. Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_25","unstructured":"Martinez, P., Gualtieri, J.A., Aguilar, P.L., P\u00e9rez, R., Linaje, M., Preciado, J.C., and Plaza, A. (2001, January 5\u20138). Hyperspectral image classification using a self-organizing map. Proceedings of the Summaries of the X JPL Airborne Earth Science Workshop, Pasadena, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3894","DOI":"10.1109\/TGRS.2007.909205","article-title":"A Time-Efficient Method for Anomaly Detection in Hyperspectral Images","volume":"45","author":"Duran","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Riese, F.M., and Keller, S. (2018, January 22\u201327). Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain. ISSN: 2153-7003.","DOI":"10.1109\/IGARSS.2018.8517812"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Keller, S., Maier, P.M., Riese, F.M., Norra, S., Holbach, A., B\u00f6rsig, N., Wilhelms, A., Moldaenke, C., Zaake, A., and Hinz, S. (2018). Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15091881"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Riese, F.M., Keller, S., and Hinz, S. (2020). Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12010007"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wong, M., Abeysinghe, W., and Hung, C.C. (2019, January 24\u201326). A Massive Self-Organizing Map For Hyperspectral Image Classification. Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands. ISSN: 2158-6276.","DOI":"10.1109\/WHISPERS.2019.8921093"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.pocean.2003.07.006","article-title":"Using self-organizing maps to identify patterns in satellite imagery","volume":"59","author":"Richardson","year":"2003","journal-title":"Prog. Oceanogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1029\/2018JC014450","article-title":"Estimation of Secondary Phytoplankton Pigments From Satellite Observations Using Self-Organizing Maps (SOMs)","volume":"124","author":"Hourany","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kristollari, V., and Karathanassi, V. (2020). Fine-Tuning Self-Organizing Maps for Sentinel-2 Imagery: Separating Clouds from Bright Surfaces. Remote Sens., 12.","DOI":"10.3390\/rs12121923"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1109\/LGRS.2019.2934503","article-title":"On the Mapping of Burned Areas and Burn Severity Using Self Organizing Map and Sentinel-2 Data","volume":"17","author":"Lasaponara","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","unstructured":"Ng, A.Y., Jordan, M.I., and Weiss, Y. (2001, January 3\u20138). On spectral clustering: Analysis and an algorithm. Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, Vancouver, BC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/LGRS.2017.2746625","article-title":"Fast Spectral Clustering With Anchor Graph for Large Hyperspectral Images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Yuan, Y., and Wang, Q. (2019). Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11040399"},{"key":"ref_38","unstructured":"T\u00fcrkmen, A.C. (2015). A Review of Nonnegative Matrix Factorization Methods for Clustering. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum Likelihood from Incomplete Data Via the EM Algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_40","unstructured":"Acito, N., Corsini, G., and Diani, M. (2003, January 21\u201325). An unsupervised algorithm for hyperspectral image segmentation based on the Gaussian mixture model. Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), Toulouse, France."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/LGRS.2013.2250905","article-title":"Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields","volume":"11","author":"Li","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/JSTARS.2018.2858008","article-title":"Unsupervised Bayesian Classification of a Hyperspectral Image Based on the Spectral Mixture Model and Markov Random Field","volume":"11","author":"Fang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s11554-008-0106-9","article-title":"Fast real-time onboard processing of hyperspectral imagery for detection and classification","volume":"4","author":"Du","year":"2009","journal-title":"J. Real-Time Image Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.3390\/rs70403966","article-title":"Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery","volume":"7","author":"Zhao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"66919","DOI":"10.1109\/ACCESS.2019.2913957","article-title":"A UAV Platform Based on a Hyperspectral Sensor for Image Capturing and On-Board Processing","volume":"7","author":"Horstrand","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10846-017-0689-0","article-title":"Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection","volume":"90","author":"Freitas","year":"2018","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4383","DOI":"10.5194\/amt-8-4383-2015","article-title":"Real-time remote detection and measurement for airborne imaging spectroscopy: A case study with methane","volume":"8","author":"Thompson","year":"2015","journal-title":"Atmos. Meas. Tech."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Alcolea, A., Paoletti, M.E., Haut, J.M., Resano, J., and Plaza, A. (2020). Inference in Supervised Spectral Classifiers for On-Board Hyperspectral Imaging: An Overview. Remote Sens., 12.","DOI":"10.3390\/rs12030534"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6448","DOI":"10.1073\/pnas.1216006110","article-title":"Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions","volume":"110","author":"Michalak","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_50","unstructured":"Gamba, P. (2004, January 20\u201324). A collection of data for urban area characterization. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_51","unstructured":"Baumgardner, M.F., Biehl, L.L., and Landgrebe, D.A. (2015). 220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992. Indian Pine Test Site, 3."},{"key":"ref_52","unstructured":"Gra\u00f1a, M., Veganzons, M.A., and Ayerdi, B. (2020, July 15). Hyperspectral Remote Sensing Scenes. Available online: www.ehu.eus\/ccwintco\/index.php\/Hyperspectral_Remote_Sensing_Scenes."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5412","DOI":"10.1109\/TIP.2014.2363423","article-title":"Spectral Unmixing via Data-guided Sparsity","volume":"23","author":"Zhu","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Prentice, E.F., Gr\u00f8tte, M.E., Sigernes, F., and Johansen, T.A. (2020, January 14\u201316). Design of a hyperspectral imager using COTS optics for small satellite applications. Proceedings of the International Conference on Space Optics\u2014ICSO 2020, Portland, OR, USA.","DOI":"10.1117\/12.2599937"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Henriksen, M.B., Garrett, J.L., Prentice, E.F., Stahl, A., Johansen, T.A., and Sigernes, F. (2019, January 24\u201326). Real-Time Corrections for a Low-Cost Hyperspectral Instrument. Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2019.8921350"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"103258","DOI":"10.1016\/j.micpro.2020.103258","article-title":"A reconfigurable multi-mode implementation of hyperspectral target detection algorithms","volume":"78","author":"Johansen","year":"2020","journal-title":"Microprocess. Microsyst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"LIII. On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Lond. Edinb. Dublin Philos. Mag. J. Sci."},{"key":"ref_58","first-page":"115","article-title":"Principal component analysis for hyperspectral image classification","volume":"62","author":"Rodarmel","year":"2002","journal-title":"Surv. Land Inf. Sci."},{"key":"ref_59","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-319-28518-4_1","article-title":"Theoretical and Applied Aspects of the Self-Organizing Maps","volume":"Volume 428","author":"Mendenhall","year":"2016","journal-title":"Advances in Self-Organizing Maps and Learning Vector Quantization"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1109\/5.537105","article-title":"Engineering applications of the self-organizing map","volume":"84","author":"Kohonen","year":"1996","journal-title":"Proc. IEEE"},{"key":"ref_63","unstructured":"Ben Khalifa, K., Girau, B., Alexandre, F., and Bedoui, M. (2004, January 6\u20138). Parallel FPGA implementation of self-organizing maps. Proceedings of the 16th International Conference on Microelectronics, Tunis, Tunisia."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Brassai, S.T. (2014, January 3\u20135). FPGA based hardware implementation of a self-organizing map. Proceedings of the 18th International Conference on Intelligent Engineering Systems INES 2014, Tihany, Hungary. ISSN: 1543-9259.","DOI":"10.1109\/INES.2014.6909349"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"De Abreu de Sousa, M.A., and Del-Moral-Hernandez, E. (2017, January 14\u201319). An FPGA distributed implementation model for embedded SOM with on-line learning. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA. ISSN: 2161-4407.","DOI":"10.1109\/IJCNN.2017.7966351"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1007\/978-3-030-61616-8_66","article-title":"A Fast Algorithm to Find Best Matching Units in Self-Organizing Maps","volume":"Volume 12397","author":"Masulli","year":"2020","journal-title":"Artificial Neural Networks and Machine Learning\u2014ICANN 2020"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4174\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:17:32Z","timestamp":1760167052000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,18]]},"references-count":66,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204174"],"URL":"https:\/\/doi.org\/10.3390\/rs13204174","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,18]]}}}