South Pole of Mars: Nature and composition of the icy terrains from Mars Express OMEGA observations

Douté, S.; Schmitt, Bernard; Langevin, Y.; Bibring, J. P.; Altieri, F.; Bellucci, G.; Gondet, B.; Poulet, F.

doi:10.1016/j.pss.2006.05.035

Cited by 68 publications

(102 citation statements)

References 22 publications

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“…To validate the results, two kinds of reference data are used (see figure 1), following one supervised classification study [5] made on the same observations: first are the reference spectra of CO 2 ice, H 2 O ice and dust, which will be noted as ψ CO2 (λ), ψ H2O (λ) and ψ dust (λ) respectively. Both reference spectra of CO 2 ice and H 2 O ice are simulations produced by a radiative transfer model in typical physical conditions of the Permanent South Polar Cap of Mars [4]. These two reference spectra are atmosphere free simulations.…”

Section: Reference Data and Classificationmentioning

confidence: 99%

“…In fact, additional knowledges can be used. In this purpose: (i) synthetic reference spectra of the main endmembers obtained after inversion [4]; (ii) a supervised classification using wavalet transform called wavanglet which is in accordance with Mars physical knowledge [5]. Finally, a last difficulty is to check the relevance of the linear mixture model as well as the hypothesis on which the algorithm is based.…”

Section: Introductionmentioning

confidence: 99%

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On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

Moussaoui¹,

et al. 2008

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The surface of Mars is currently being imaged with an unprecedented combination of spectral and spatial resolution. This high resolution, and its spectral range, give the ability to pinpoint chemical species on the surface and the atmosphere of Mars moreaccurately than before. The subject of this paper is to present a method to extract informations on these chemicals from hyperspectral images. A first approach, based on Independent Component Analysis (ICA) [1], is able to extract artifacts and locations of CO2 and H2O ices. However, the main independence assumption and some basic properties (like the positivity of images and spectra) being unverified, the reliability of all the independent components (ICs) is weak. For improving the component extraction and consequently the endmember classification, a combination of spatial ICA with spectral Bayesian Positive Source Separation (BPSS) [2] is proposed. To reduce the computational burden, the basic idea is to use spatial ICA yielding a rough classification of pixels, which allows selection of small, but relevant, number of pixels and then BPSS is applied for the estimation of the source spectra using the spectral mixtures provided by this reduced set of pixels. Finally, the abundances of the components is assessed on the whole pixels of the images. Results of this approach are shown and evaluated by comparison with available reference spectra.

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Section: Reference Data and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

Moussaoui¹,

et al. 2008

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“…The seasonal CO 2 cycle, including formation of the seasonal caps, has been extensively studied (recent references include, e.g., James and Cantor, 2001;Kieffer and Titus, 2001;Smith et al, 2001;Piquex et al, 2003;Douté et al, 2006). Each year, seasonal insolation variations result in about 25% of the martian atmosphere alternatively condensing and sublimating from the poles.…”

Section: What Is the Current Mass Balance (Mechanisms Rates Temporamentioning

confidence: 99%

Introduction to the 4th Mars Polar Science and Exploration Conference special issue: Five top questions in Mars polar science

Fishbaugh

Hvidberg

Beaty

et al. 2008

Icarus

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“…The OMEGA instrument provides a global coverage at a medium resolution (1-4 km) of the entire surface of Mars, as well as snapshots of selected areas with a resolution of a few hundreds of meters. OMEGA has provided a wealth of information concerning the composition and distribution of minerals and volatile species at the surface and in the atmosphere of Mars through the spectral analysis of the re-diffused solar light [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Exploration of Planetary Hyperspectral Images with Unsupervised Spectral Unmixing: A Case Study of Planet Mars

Luo

Douté

et al. 2018

Remote Sensing

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Abstract: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 (≈300 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.

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South Pole of Mars: Nature and composition of the icy terrains from Mars Express OMEGA observations

Cited by 68 publications

References 22 publications

On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

Introduction to the 4th Mars Polar Science and Exploration Conference special issue: Five top questions in Mars polar science

Exploration of Planetary Hyperspectral Images with Unsupervised Spectral Unmixing: A Case Study of Planet Mars

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