{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:56:05Z","timestamp":1764251765149,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Europlanet H204 RI","award":["871149"],"award-info":[{"award-number":["871149"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["871149"],"award-info":[{"award-number":["871149"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Four-band color imaging of the Martian surface using the Color and Stereo Surface Imaging System (CaSSIS) onboard the European Space Agency\u2019s ExoMars Trace Gas Orbiter exhibits a high color diversity in specific regions. Not only is the correlation of color diversity maps with local morphological properties desirable, but mineralogical interpretation of the observations is also of great interest. The relatively high spatial resolution of CaSSIS data mitigates its low spectral resolution. In this paper, we combine the broad-band imaging of the surface of Mars, acquired by CaSSIS with hyperspectral data from the Compact Reconnaissance Imaging Spectrometer (CRISM) onboard NASA\u2019s Mars Reconnaissance Orbiter to achieve a fusion of both datasets. We achieve this using dimensionality reduction and data clustering of the high dimensional datasets from CRISM. In the presented research, CRISM data from the Coprates Chasma region of Mars are tested with different machine learning methods and compared for robustness. With the help of a suitable metric, the best method is selected and, in a further step, an optimal cluster number is determined. To validate the methods, the so-called \u201csummary products\u201d derived from the hyperspectral data are used to correlate each cluster with its mineralogical properties. We restrict the analysis to the visible range in order to match the generated clusters to the CaSSIS band information in the range of 436\u20131100 nm. In the machine learning community, the so-called UMAP method for dimensionality reduction has recently gained attention because of its speed compared to the already established t-SNE. The results of this analysis also show that this method in combination with the simple K-Means outperforms comparable methods in its efficiency and speed. The cluster size obtained is between three and six clusters. Correlating the spectral cluster maps with the given summary products from CRISM shows that four bands, and especially the NIR bands and VIS albedo, are sufficient to discriminate most of these clusters. This demonstrates that features in the four-band CaSSIS images can provide robust mineralogical information, despite the limited spectral information using semi-automatic processing.<\/jats:p>","DOI":"10.3390\/rs14112524","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T08:41:33Z","timestamp":1653468093000},"page":"2524","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Generation and Optimization of Spectral Cluster Maps to Enable Data Fusion of CaSSIS and CRISM Datasets"],"prefix":"10.3390","volume":"14","author":[{"given":"Michael","family":"Fernandes","sequence":"first","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]},{"given":"Alexander","family":"Pletl","sequence":"additional","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]},{"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, Jacobs University Bremen, 28759 Bremen, Germany"}]},{"given":"Benedikt","family":"Elser","sequence":"additional","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E05S02","DOI":"10.1029\/2005JE002605","article-title":"Mars reconnaissance orbiter\u2019s high resolution imaging science experiment (HiRISE)","volume":"112","author":"McEwen","year":"2007","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"E05S02","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. Geophys. Res. Planets"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1007\/s11214-017-0421-1","article-title":"The Colour and Stereo Surface Imaging System (CaSSIS) for the ExoMars Trace Gas Orbiter","volume":"212","author":"Thomas","year":"2017","journal-title":"Space Sci. Rev."},{"key":"ref_4","unstructured":"Schubert, G. (2015). Treatise on Geophysics, Elsevier."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gao, A.F., Rasmussen, B., Kulits, P., Scheller, E.L., Greenberger, R., and Ehlmann, B.L. (2021, January 20\u201325). Generalized Unsupervised Clustering of Hyperspectral Images of Geological Targets in the Near Infrared. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00485"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1198\/jasa.2003.s308","article-title":"Principal Component Analysis","volume":"98","author":"Timmerman","year":"2003","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Martel, E., Lazcano, R., L\u00f3pez, J., Madro\u00f1al, D., Salvador, R., L\u00f3pez, S., Juarez, E., Guerra, R., Sanz, C., and Sarmiento, R. (2018). Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons. Remote Sens., 10.","DOI":"10.3390\/rs10060864"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Melit Devassy, B., George, S., and Nussbaum, P. (2020). Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE. J. Imaging, 6.","DOI":"10.3390\/jimaging6050029"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1515\/pac-2017-0907","article-title":"Innovative data reduction and visualization strategy for hyperspectral imaging datasets using t-SNE approach","volume":"90","author":"Pouyet","year":"2018","journal-title":"Pure Appl. Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1007\/s11042-018-5715-0","article-title":"Improved t-SNE based manifold dimensional reduction for remote sensing data processing","volume":"78","author":"Song","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4910","DOI":"10.1364\/AO.26.004910","article-title":"Adaptive, associative, and self-organizing functions in neural computing","volume":"26","author":"Kohonen","year":"1987","journal-title":"Appl. Opt."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Picollo, M., Cucci, C., Casini, A., and Stefani, L. (2020). Hyper-Spectral Imaging Technique in the Cultural Heritage Field: New Possible Scenarios. Sensors, 20.","DOI":"10.3390\/s20102843"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1039\/C9AY02483B","article-title":"Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples","volume":"12","author":"Wander","year":"2020","journal-title":"Anal. Methods"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Helbert, J., D\u2019Amore, M., Aye, M., and Kerner, H. (2022). Chapter 7\u2014Automated surface mapping via unsupervised learning and classification of Mercury Visible\u2013Near-Infrared reflectance spectra. Machine Learning for Planetary Science, Elsevier.","DOI":"10.1016\/B978-0-12-818721-0.00016-1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1002\/cyto.a.24016","article-title":"High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data","volume":"97","author":"Mayer","year":"2020","journal-title":"Cytom. Part A"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109442","DOI":"10.1016\/j.celrep.2021.109442","article-title":"Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data","volume":"36","author":"Yang","year":"2021","journal-title":"Cell Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e2021JE006918","DOI":"10.1029\/2021JE006918","article-title":"Spectral units analysis of quadrangle H05-Hokusai on Mercury","volume":"27","author":"Zambon","year":"2022","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Massironi, M., Rossi, A.P., Wright, J., Zambon, F., Poheler, C., Giacomini, L., Carli, C., Ferrari, S., Semenzato, A., and Luzzi, E. (2021, January 14\u201315). From Morpho-Stratigraphic to Geo(Spectro)-Stratigraphic Units: The PLANMAP Contribution. Proceedings of the 2021 Annual Meeting of Planetary Geologic Mappers, Virtual. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2021LPICo2610.7045M.","DOI":"10.5194\/egusphere-egu21-15675"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Semenzato, A., Massironi, M., Ferrari, S., Galluzzi, V., Rothery, D.A., Pegg, D.L., Pozzobon, R., and Marchi, S. (2020). An Integrated Geologic Map of the Rembrandt Basin, on Mercury, as a Starting Point for Stratigraphic Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12193213"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Giacomini, L., Carli, C., Zambon, F., Galluzzi, V., Ferrari, S., Massironi, M., Altieri, F., Ferranti, L., Palumbo, P., and Capaccioni, F. (2021, January 19\u201330). Integration between morphological and spectral characteristics for the geological map of Kuiper quadrangle (H06). Proceedings of the EGU General Assembly Conference, Online.","DOI":"10.5194\/egusphere-egu21-15052"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105136","DOI":"10.1016\/j.pss.2020.105136","article-title":"Lermontov crater on Mercury: Geology, morphology and spectral properties of the coexisting hollows and pyroclastic deposits","volume":"195","author":"Pajola","year":"2021","journal-title":"Planet. Space Sci."},{"key":"ref_23","unstructured":"Seelos, F. (2016). Mars Reconnaissance Orbiter Compact Reconnaissance Imaging Spectrometer for Mars Map-Projected Targeted Reduced Data Record, MRO-M-CRISM-5-RDR-MPTARGETED-V1.0."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"E08S14","DOI":"10.1029\/2006JE002831","article-title":"CRISM multispectral summary products: Parameterizing mineral diversity on Mars from reflectance","volume":"112","author":"Pelkey","year":"2007","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1002\/2014JE004627","article-title":"Revised CRISM spectral parameters and summary products based on the currently detected mineral diversity on Mars","volume":"119","author":"Viviano","year":"2014","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s11214-017-0436-7","article-title":"Image Simulation and Assessment of the Colour and Spatial Capabilities of the Colour and Stereo Surface Imaging System (CaSSIS) on the ExoMars Trace Gas Orbiter","volume":"214","author":"Tornabene","year":"2018","journal-title":"Space Sci. Rev."},{"key":"ref_27","unstructured":"Parkes Bowen, A., Mandon, L., Bridges, J., Quantin-Nataf, C., Tornabene, L., Page, J., Briggs, J., Thomas, N., and Cremonese, G. (October, January 21). Using band ratioed CaSSIS imagery and analysis of fracture morphology to characterise Oxia Planum\u2019s clay-bearing unit. Proceedings of the European Planetary Science Congress, Virtual."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105429","DOI":"10.1016\/j.pss.2022.105429","article-title":"A CaSSIS and HiRISE map of the Clay-bearing Unit at the ExoMars 2022 landing site in Oxia Planum","volume":"214","author":"Bridges","year":"2022","journal-title":"Planet. Space Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105394","DOI":"10.1016\/j.pss.2021.105394","article-title":"Absolute calibration of the Colour and Stereo Surface Imaging System (CaSSIS)","volume":"211","author":"Thomas","year":"2021","journal-title":"Planet. Space Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.asr.2017.10.025","article-title":"Geometric calibration of Colour and Stereo Surface Imaging System of ESA\u2019s Trace Gas Orbiter","volume":"61","author":"Tulyakov","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.icarus.2006.05.024","article-title":"Formation of a terraced fan deposit in Coprates Catena, Mars","volume":"184","author":"Weitz","year":"2006","journal-title":"Icarus"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.icarus.2017.10.030","article-title":"Stepped fans and facies-equivalent phyllosilicates in Coprates Catena, Mars","volume":"307","author":"Grindrod","year":"2018","journal-title":"Icarus"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"E12005","DOI":"10.1029\/2007JE003070","article-title":"Geological context of water-altered minerals in Valles Marineris, Mars","volume":"113","author":"Chojnacki","year":"2008","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1002\/2015JE004954","article-title":"Stratigraphy and formation of clays, sulfates, and hydrated silica within a depression in Coprates Catena, Mars","volume":"121","author":"Weitz","year":"2016","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Murchie, S.L., Bibring, J.P., Arvidson, R.E., Bishop, J.L., Carter, J., Ehlmann, B.L., Langevin, Y., Mustard, J.F., Poulet, F., and Riu, L. (2019). Visible to Short-Wave Infrared Spectral Analyses of Mars from Orbit Using CRISM and OMEGA. Remote Compositional Analysis: Techniques for Understanding Spectroscopy, Mineralogy, and Geochemistry of Planetary Surfaces, Cambridge University Press. Cambridge Planetary Science.","DOI":"10.1017\/9781316888872.025"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.epsl.2009.11.004","article-title":"Structural analysis of interior layered deposits in Northern Coprates Chasma, Mars","volume":"294","author":"Fueten","year":"2010","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e2019JE006043","DOI":"10.1029\/2019JE006043","article-title":"Anomalous Phyllosilicate-Bearing Outcrops South of Coprates Chasma: A Study of Possible Emplacement Mechanisms","volume":"125","author":"Buczkowski","year":"2020","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_38","first-page":"E00J05","article-title":"Extensive surface pedogenic alteration of the Martian Noachian crust evidenced by plateau phyllosilicates around Valles Marineris","volume":"117","author":"Flahaut","year":"2012","journal-title":"J. Geophys. Res."},{"key":"ref_39","unstructured":"Kovenko, V., and Bogach, I. (2020, January 2\u20133). A Comprehensive Study of Autoencoders\u2019 Applications Related to Images. Proceedings of the IT&I Workshops, Kyiv, Ukraine."},{"key":"ref_40","first-page":"1","article-title":"A tutorial on deep learning part 2: Autoencoders, convolutional neural networks and recurrent neural networks","volume":"20","author":"Le","year":"2015","journal-title":"Google Brain"},{"key":"ref_41","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On Information and Sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"El Moataz, A., Mammass, D., Mansouri, A., and Nouboud, F. (2020). Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study. Image and Signal Processing, Springer International Publishing.","DOI":"10.1007\/978-3-030-51935-3"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"119547","DOI":"10.1016\/j.saa.2021.119547","article-title":"Application of Uniform Manifold Approximation and Projection (UMAP) in spectral imaging of artworks","volume":"252","author":"Vermeulen","year":"2021","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_46","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"Volume 1","author":"MacQueen","year":"1967","journal-title":"Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"ref_47","unstructured":"Bishop, C.M., and Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"FCM: The fuzzy c-means clustering algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Comput. Geosci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kohonen, T. (1997). Self-Organizing Maps, Springer.","DOI":"10.1007\/978-3-642-97966-8"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TGRS.2008.918089","article-title":"Hyperspectral Subspace Identification","volume":"46","author":"Nascimento","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","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_52","unstructured":"(2022, March 17). Sklearn-Som v. 1.1.0 Master Documentation. Available online: https:\/\/sklearn-som.readthedocs.io\/en\/latest\/."},{"key":"ref_53","unstructured":"Dias, M.L.D. (2019). Fuzzy-c-Means: An Implementation of Fuzzy C-Means Clustering Algorithm, Zenodo."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat.-Theory Methods"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A Cluster Separation Measure","volume":"PAMI-1","author":"Davies","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/BF02294245","article-title":"An examination of procedures for determining the number of clusters in a data set","volume":"50","author":"Milligan","year":"1985","journal-title":"Psychometrika"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.icarus.2017.11.002","article-title":"Quantifying widespread aqueous surface weathering on Mars: The plateaus south of Coprates Chasma","volume":"302","author":"Loizeau","year":"2018","journal-title":"Icarus"},{"key":"ref_59","first-page":"99122Y","article-title":"Thin-film optical pass band filters based on new photo-lithographic process for CaSSIS FPA detector on Exomars TGO mission: Development, integration, and test","volume":"Volume 9912","author":"Gambicorti","year":"2016","journal-title":"Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation II"},{"key":"ref_60","first-page":"105620A","article-title":"First light of Cassis: The stereo surface imaging system onboard the exomars TGO","volume":"Volume 10562","author":"Gambicorti","year":"2017","journal-title":"Proceedings of the International Conference on Space Optics\u2014ICSO 2016"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2524\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:18:13Z","timestamp":1760138293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2524"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":60,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112524"],"URL":"https:\/\/doi.org\/10.3390\/rs14112524","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}