{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:56:42Z","timestamp":1764251802293,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Europlanet","award":["H204 RI","871149"],"award-info":[{"award-number":["H204 RI","871149"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["H204 RI","871149"],"award-info":[{"award-number":["H204 RI","871149"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we expand upon our previous research on unsupervised learning algorithms to map the spectral parameters of the Martian surface. Previously, we focused on the VIS-NIR range of hyperspectral data from the CRISM imaging spectrometer instrument onboard NASA\u2019s Mars Reconnaissance Orbiter to relate to other correspondent imager data sources. In this study, we generate spectral cluster maps on a selected CRISM datacube in a NIR range of 1050\u20132550 nm. This range is suitable for identifying most dominate mineralogy formed in ancient wet environment such as phyllosilicates, pyroxene and smectites. In the machine learning community, the UMAP method for dimensionality reduction has recently gained attention because of its computing efficiency and speed. We apply this algorithm in combination with k-Means to data from Jezero Crater. Such studies of Jezero Crater are of priority to support the planning of the current NASA\u2019s Perseversance rover mission. We compare our results with other methodologies based on a suitable metric and can identify an optimal cluster size of six for the selected datacube. Our proposed approach outperforms comparable methods in efficiency and speed. To show the geological relevance of the different clusters, the so-called \u201csummary products\u201d derived from the hyperspectral data are used to correlate each cluster with its mineralogical properties. We show that clustered regions relate to different mineralogical compositions (e.g., carbonates and pyroxene). Finally the generated spectral cluster map shows a qualitatively strong resemblance with a given manually compositional expert map. As a conclusion, the presented method can be implemented for automated region-based analysis to extend our understanding of Martian geological history.<\/jats:p>","DOI":"10.3390\/rs15040939","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T02:55:54Z","timestamp":1675911354000},"page":"939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Spectral Clustering of CRISM Datasets in Jezero Crater Using UMAP and k-Means"],"prefix":"10.3390","volume":"15","author":[{"given":"Alexander","family":"Pletl","sequence":"first","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]},{"given":"Michael","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0146-0071","authenticated-orcid":false,"given":"Nicolas","family":"Thomas","sequence":"additional","affiliation":[{"name":"Physikalisches Institut, University of Bern, 3012 Bern, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0137-1984","authenticated-orcid":false,"given":"Angelo Pio","family":"Rossi","sequence":"additional","affiliation":[{"name":"Department of Physics and Earth Sciences, Constructor University, 28759 Bremen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6029-4424","authenticated-orcid":false,"given":"Benedikt","family":"Elser","sequence":"additional","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E05S03","DOI":"10.1029\/2006JE002682","article-title":"Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Reconnaissance Orbiter (MRO)","volume":"112","author":"Murchie","year":"2007","journal-title":"J. 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