{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T00:26:08Z","timestamp":1770423968236,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Texas National Security Network Excellence Fund award for Environmental Sensing Security Sentinels","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}]},{"name":"Texas National Security Network Excellence Fund award for Environmental Sensing Security Sentinels","award":["NSF #2019135"],"award-info":[{"award-number":["NSF #2019135"]}]},{"name":"Texas National Security Network Excellence Fund award for Environmental Sensing Security Sentinels","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}]},{"name":"SOFWERX","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}]},{"name":"SOFWERX","award":["NSF #2019135"],"award-info":[{"award-number":["NSF #2019135"]}]},{"name":"SOFWERX","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["NSF #2019135"],"award-info":[{"award-number":["NSF #2019135"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Texas at Dallas Office of Sponsored Programs","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}]},{"name":"University of Texas at Dallas Office of Sponsored Programs","award":["NSF #2019135"],"award-info":[{"award-number":["NSF #2019135"]}]},{"name":"University of Texas at Dallas Office of Sponsored Programs","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}]},{"name":"Dean of Natural Sciences and Mathematics","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}]},{"name":"Dean of Natural Sciences and Mathematics","award":["NSF #2019135"],"award-info":[{"award-number":["NSF #2019135"]}]},{"name":"Dean of Natural Sciences and Mathematics","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}]},{"name":"Chair of the Physics Department","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}]},{"name":"Chair of the Physics Department","award":["NSF #2019135"],"award-info":[{"award-number":["NSF #2019135"]}]},{"name":"Chair of the Physics Department","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}]},{"name":"TRECIS CC* Cyberteam","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}]},{"name":"TRECIS CC* Cyberteam","award":["NSF #2019135"],"award-info":[{"award-number":["NSF #2019135"]}]},{"name":"TRECIS CC* Cyberteam","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}]},{"name":"EPA P3","award":["OAC-2115094"],"award-info":[{"award-number":["OAC-2115094"]}]},{"name":"EPA P3","award":["NSF #2019135"],"award-info":[{"award-number":["NSF #2019135"]}]},{"name":"EPA P3","award":["84057001-0"],"award-info":[{"award-number":["84057001-0"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicles equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how Generative Topographic Mapping (GTM), a probabilistic realization of the self-organizing map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply GTM to visualize the distribution of captured reflectance spectra, revealing the small-scale spatial variability of the water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of nearshore algae, as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source.<\/jats:p>","DOI":"10.3390\/rs16132430","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T09:01:39Z","timestamp":1719910899000},"page":"2430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5910-0183","authenticated-orcid":false,"given":"John","family":"Waczak","sequence":"first","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0126-218X","authenticated-orcid":false,"given":"Adam","family":"Aker","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2688-648X","authenticated-orcid":false,"given":"Lakitha O. H.","family":"Wijeratne","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9841-6703","authenticated-orcid":false,"given":"Shawhin","family":"Talebi","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0667-2345","authenticated-orcid":false,"given":"Ashen","family":"Fernando","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2657-3416","authenticated-orcid":false,"given":"Prabuddha M. H.","family":"Dewage","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Mazhar","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Matthew","family":"Lary","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"David","family":"Schaefer","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"given":"Gokul","family":"Balagopal","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4265-9543","authenticated-orcid":false,"given":"David J.","family":"Lary","sequence":"additional","affiliation":[{"name":"Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0034-4257(01)00238-3","article-title":"Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data","volume":"79","author":"Koponen","year":"2002","journal-title":"Remote. Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"695","DOI":"10.14358\/PERS.69.6.695","article-title":"Remote sensing techniques to assess water quality","volume":"69","author":"Ritchie","year":"2003","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote. Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Arroyo-Mora, J.P., Kalacska, M., Inamdar, D., Soffer, R., Lucanus, O., Gorman, J., Naprstek, T., Schaaf, E.S., Ifimov, G., and Elmer, K. (2019). Implementation of a UAV\u2013hyperspectral pushbroom imager for ecological monitoring. Drones, 3.","DOI":"10.3390\/drones3010012"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4136","DOI":"10.1080\/01431161.2020.1714771","article-title":"UAV-hyperspectral imaging of spectrally complex environments","volume":"41","author":"Banerjee","year":"2020","journal-title":"Int. J. Remote. Sens."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1017\/S1466046615000459","article-title":"Near-remote sensing of water turbidity using small unmanned aircraft systems","volume":"18","author":"Vogt","year":"2016","journal-title":"Environ. Pract."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zeng, S., and He, W. (2022). Selection and quantification of best water quality indicators using UAV-mounted hyperspectral data: A case focusing on a local river network in Suzhou City, China. Sustainability, 14.","DOI":"10.3390\/su142316226"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Lu, Q., Si, W., Wei, L., Li, Z., Xia, Z., Ye, S., and Xia, Y. (2021). Retrieval of water quality from UAV-borne hyperspectral imagery: A comparative study of machine learning algorithms. Remote. Sens., 13.","DOI":"10.3390\/rs13193928"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lary, D.J., Schaefer, D., Waczak, J., Aker, A., Barbosa, A., Wijeratne, L.O., Talebi, S., Fernando, B., Sadler, J., and Lary, T. (2021). Autonomous learning of new environments with a robotic team employing hyper-spectral remote sensing, comprehensive in-situ sensing and machine learning. Sensors, 21.","DOI":"10.20944\/preprints202102.0454.v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Waczak, J., Aker, A., Wijeratne, L.O., Talebi, S., Fernando, B., Hathurusinghe, P., Iqbal, M., Schaefer, D., and Lary, D.J. (2024). Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In-Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction. Remote. Sens., 16.","DOI":"10.20944\/preprints202401.2041.v1"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Parra, L., Ahmad, A., Sendra, S., Lloret, J., and Lorenz, P. (2024). Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity. Chemosensors, 12.","DOI":"10.3390\/chemosensors12030034"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chirchi, V., Chirchi, E., Khushi, E.C., Bairavi, S.M., and Indu, K.S. (March, January 28). Optical Sensor for Water Bacteria Detection using Machine Learning. Proceedings of the 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India.","DOI":"10.23919\/INDIACom61295.2024.10498622"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/TGRS.2003.808879","article-title":"Principal-components-based display strategy for spectral imagery","volume":"41","author":"Tyo","year":"2003","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, B., and Yu, X. (November, January 31). Hyperspectral image visualization using t-distributed stochastic neighbor embedding. Proceedings of the MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, Enshi, China.","DOI":"10.1117\/12.2205840"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/JSTARS.2014.2320576","article-title":"A review of nonlinear hyperspectral unmixing methods","volume":"7","author":"Heylen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: A fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4414","DOI":"10.1109\/JSTARS.2022.3175257","article-title":"Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review","volume":"15","author":"Feng","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1109\/JSTSP.2015.2413371","article-title":"Unsupervised nearest neighbors clustering with application to hyperspectral images","volume":"9","author":"Cariou","year":"2015","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4309","DOI":"10.1109\/TGRS.2018.2890633","article-title":"DAEN: Deep autoencoder networks for hyperspectral unmixing","volume":"57","author":"Su","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TCI.2019.2948726","article-title":"Deep generative endmember modeling: An application to unsupervised spectral unmixing","volume":"6","author":"Borsoi","year":"2019","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TGRS.2020.2992743","article-title":"Convolutional autoencoder for spectral\u2013spatial hyperspectral unmixing","volume":"59","author":"Palsson","year":"2020","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/5.58325","article-title":"The self-organizing map","volume":"78","author":"Kohonen","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cantero, M., Perez, R., Martinez, P.J., Aguilar, P., Plaza, J., and Plaza, A. (2004, January 27\u201328). Analysis of the behavior of a neural network model in the identification and quantification of hyperspectral signatures applied to the determination of water quality. Proceedings of the Chemical and Biological Standoff Detection II SPIE, Philadelphia, PA, USA.","DOI":"10.1117\/12.580058"},{"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":"Ceylan, O., and Kaya, G.T. (2021, January 11\u201316). Feature Selection Using Self Organizing Map Oriented Evolutionary Approach. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553491"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Riese, F.M., Keller, S., and Hinz, S. (2019). Supervised and semi-supervised self-organizing maps for regression and classification focusing on hyperspectral data. Remote. Sens., 12.","DOI":"10.3390\/rs12010007"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Danielsen, A.S., Johansen, T.A., and Garrett, J.L. (2021). Self-organizing maps for clustering hyperspectral images on-board a cubesat. Remote. Sens., 13.","DOI":"10.3390\/rs13204174"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1162\/089976698300017953","article-title":"GTM: The generative topographic mapping","volume":"10","author":"Bishop","year":"1998","journal-title":"Neural Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1002\/minf.201100163","article-title":"Generative topographic mapping (GTM): Universal tool for data visualization, structure-activity modeling and dataset comparison","volume":"31","author":"Kireeva","year":"2012","journal-title":"Mol. Inform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1021\/ci500575y","article-title":"Chemical data visualization and analysis with incremental generative topographic mapping: Big data challenge","volume":"55","author":"Gaspar","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.ddtec.2020.06.003","article-title":"Generative topographic mapping in drug design","volume":"32","author":"Horvath","year":"2019","journal-title":"Drug Discov. Today Technol."},{"key":"ref_34","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"},{"key":"ref_35","unstructured":"Waczak, J. (2024, April 24). GenerativeTopographicMapping.jl. Available online: https:\/\/zenodo.org\/records\/11061258."},{"key":"ref_36","unstructured":"Bezanson, J., Karpinski, S., Shah, V.B., and Edelman, A. (2012). Julia: A fast dynamic language for technical computing. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Blaom, A.D., Kiraly, F., Lienart, T., Simillides, Y., Arenas, D., and Vollmer, S.J. (2020). MLJ: A Julia package for composable machine learning. arXiv.","DOI":"10.21105\/joss.02704"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ruddick, K.G., Voss, K., Banks, A.C., Boss, E., Castagna, A., Frouin, R., Hieronymi, M., Jamet, C., Johnson, B.C., and Kuusk, J. (2019). A review of protocols for fiducial reference measurements of downwelling irradiance for the validation of satellite remote sensing data over water. Remote. Sens., 11.","DOI":"10.3390\/rs11151742"},{"key":"ref_39","first-page":"148","article-title":"A program for direct georeferencing of airborne and spaceborne line scanner images","volume":"34","author":"Muller","year":"2002","journal-title":"Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_40","unstructured":"B\u00e4umker, M., and Heimes, F. (2001, January 17\u201318). New calibration and computing method for direct georeferencing of image and scanner data using the position and angular data of an hybrid inertial navigation system. Proceedings of the OEEPE Workshop, Integrated Sensor Orientation, Hannover, Germany."},{"key":"ref_41","first-page":"1417","article-title":"A multi-sensor system for airborne image capture and georeferencing","volume":"66","author":"Mostafa","year":"2000","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote. Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/JSTARS.2010.2086435","article-title":"Normalized Spectral Similarity Score (NS3) as an Efficient Spectral Library Searching Method for Hyperspectral Image Classification","volume":"4","author":"Nidamanuri","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2018). Hyperspectral Indices and Image Classifications for Agriculture and Vegetation, CRC Press.","DOI":"10.1201\/9781315159331"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111780","DOI":"10.1016\/j.rse.2020.111780","article-title":"Can UAVs fill the gap between in situ surveys and satellites for habitat mapping?","volume":"243","author":"Houet","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5523012","DOI":"10.1109\/TGRS.2023.3307346","article-title":"Intrinsic Decomposition Embedded Spectral Unmixing for Satellite Hyperspectral Images with Endmembers From UAV Platform","volume":"61","author":"Gu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Balas, E. (2007). The prize collecting traveling salesman problem and its applications. The Traveling Salesman Problem and Its Variations, Springer.","DOI":"10.1007\/0-306-48213-4_14"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/TRO.2020.2994003","article-title":"Learning a spatial field in minimum time with a team of robots","volume":"36","author":"Suryan","year":"2020","journal-title":"IEEE Trans. Robot."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Han, T., and Goodenough, D.G. (2007, January 23\u201328). Investigation of nonlinearity in hyperspectral remotely sensed imagery\u2014A nonlinear time series analysis approach. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423107"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0925-2312(98)00043-5","article-title":"Developments of the generative topographic mapping","volume":"21","author":"Bishop","year":"1998","journal-title":"Neurocomputing"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/02564602.2020.1740615","article-title":"PCA-based feature reduction for hyperspectral remote sensing image classification","volume":"38","author":"Uddin","year":"2021","journal-title":"IETE Tech. Rev."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2430\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:09:09Z","timestamp":1760108949000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,2]]},"references-count":51,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132430"],"URL":"https:\/\/doi.org\/10.3390\/rs16132430","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,2]]}}}