{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:52:46Z","timestamp":1771336366289,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T00:00:00Z","timestamp":1525910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ANR project XIMRI\/NSFC","award":["41061130553"],"award-info":[{"award-number":["41061130553"]}]},{"name":"Chinese project NSFC","award":["61261130587"],"award-info":[{"award-number":["61261130587"]}]},{"name":"Chinese project NSFC","award":["61571332"],"award-info":[{"award-number":["61571332"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We propose to replace traditional spectral index methods by unsupervised spectral unmixing methods for the exploration of large datasets of planetary hyperspectral images. The main goal of this article is to test the ability of these analysis techniques to automatically extract the spectral signatures of the species present on the surface and to map their abundances accurately and with an acceptable processing time. We consider observations of the surface of Mars acquired by the imaging spectrometer OMEGA aboard MEX as a case study. The moderate spatial resolution (\u2248300 m\/pixel at best) of this instrument implies the systematic existence of geographical mixtures possibly conjugated with non-linear (e.g., intimate) mixtures. We examine the sensitivity of a series of state-of-the-art methods of unmixing to the intrinsic spectral variability of the species in the image and to intimate assemblages of compounds. This study is made possible thanks to the use of well-controlled synthetic data and a real OMEGA image, for which the present icy species (water and carbon dioxide ices) and their characteristic spectra are widely known by the planetary community. Furthermore, reference maps of component abundances are built by the inversion of a more realistic physical model (simulating the propagation of solar light through the atmosphere and reflected back to the sensor) in order to validate the methods with the real image by comparison with the maps extracted by unmixing. The results produced by the processing pipeline of the eigenvalue likelihood maximization (ELM), vertex component analysis (VCA) and non-negativity condition least squares error estimators (NNLS) are the most robust to non-linear effects, highly-mixed pixels and different types of mixtures. Despite this fact, the produced results are not always the best because the VCA method assumes the existence of pure pixels in the image, that is pixels completely occupied by a single species. However, this pipeline is very fast and provides endmember spectra that are always interpretable. Finally, it produces more accurate distribution maps than the spectral index methods. More generally, the potential benefits of unsupervised spectral unmixing methods in planetary exploration is emphasized.<\/jats:p>","DOI":"10.3390\/rs10050737","type":"journal-article","created":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T03:42:48Z","timestamp":1526010168000},"page":"737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Exploration of Planetary Hyperspectral Images with Unsupervised Spectral Unmixing: A Case Study of Planet Mars"],"prefix":"10.3390","volume":"10","author":[{"given":"Jun","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Bin","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Sylvain","family":"Dout\u00e9","sequence":"additional","affiliation":[{"name":"Institut de plan\u00e9tologie et d\u2019astrophysique de Grenoble, CNRS, 38000 Grenoble, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-2875","authenticated-orcid":false,"given":"Jocelyn","family":"Chanussot","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France"},{"name":"Faculty of Electrical and Computer Engineering, University of Iceland, 101 Reykjavik, Iceland"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1126\/science.279.5357.1692","article-title":"Results from the Mars Global Surveyor Thermal Emission Spectrometer","volume":"279","author":"Christensen","year":"1998","journal-title":"Science"},{"key":"ref_2","first-page":"1118","article-title":"Spectral Feature Mapping with Mars Global Surveyor Thermal Emission Spectra: Mineralogic Implications","volume":"Volume 32","author":"Clark","year":"2000","journal-title":"Bulletin of the American Astronomical Society"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7719","DOI":"10.1029\/92JE00453","article-title":"Thermal emission spectrometer experiment: Mars Observer mission","volume":"97","author":"Christensen","year":"1992","journal-title":"J. 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