Mapping surface materials on Mars from Mars Pathfinder spectral images with HYPEREYE

Merényi, Erzsébet; Farrand, W. H.; Tracadas, P. W.

doi:10.1109/itcc.2004.1286720

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Real examples are the detection of rare mineralogy in Mars Pathfinder multispectral imagery, 8 or finding very small spectrally distinct, 3-10 pixel spatial objects from an urban multi-and hyperspectral images. Importantly, one does not need to know the data distribution, not even the fact that rare clusters exist in the data, for the magnification to take place; thus SOM magnification is truly a discovery tool.…”

Section: Background On Self-organizing Mapsmentioning

confidence: 99%

Intelligent information extraction to aid science decision making in autonomous space exploration

2008

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Effective scientific exploration of remote targets such as solar system objects increasingly calls for autonomous data analysis and decision making on-board. Today, robots in space missions are programmed to traverse from one location to another without regard to what they might be passing by. By not processing data as they travel, they can miss important discoveries, or will need to travel back if scientists on Earth find the data warrant backtracking. This is a suboptimal use of resources even on relatively close targets such as the Moon or Mars. The farther mankind ventures into space, the longer the delay in communication, due to which interesting findings from data sent back to Earth are made too late to command a (roving, floating, or orbiting) robot to further examine a given location. However, autonomous commanding of robots in scientific exploration can only be as reliable as the scientific information extracted from the data that is collected and provided for decision making. In this paper, we focus on the discovery scenario, where information extraction is accomplished with unsupervised clustering. For high-dimensional data with complicated structure, detailed segmentation that identifies all significant groups and discovers the small, surprising anomalies in the data, is a challenging task at which conventional algorithms often fail. We approach the problem with precision manifold learning using self-organizing neural maps with non-standard features developed in the course of our research. We demonstrate the effectiveness and robustness of this approach on multi-spectral imagery from the Mars Exploration Rovers Pancam, and on synthetic hyperspectral imagery.

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Section: Background On Self-organizing Mapsmentioning

confidence: 99%

Intelligent information extraction to aid science decision making in autonomous space exploration

2008

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“…A hiperspektrális technika alkalmazható más égitestek vizsgálatára is, így tudományos igényű felvételek készültek a Holdról, a Szaturnuszról és a Marsról is (Chauhan et al 2015, Merényi et al 2004). Az így nyert képek értelmezéséhez olyan, földi referenciaterületet érdemes tanulmányozni, melynek litológiai, hőmérsékleti és egyéb környezeti jellemzői hasonlítanak a vizsgált bolygóéhoz.…”

Section: Hiperspektrális Távérzékelés Alkalmazása Tájvizsgálatokbanunclassified

Hiperspektrális távérzékelési módszerek alkalmazhatósága tájvizsgálatokban

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Onboard Detection of Jarosite Minerals with Applications to Mars

Bornstein

Castaño

Gilmore³

et al.

2006 IEEE Aerospace Conference

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Mapping surface materials on Mars from Mars Pathfinder spectral images with HYPEREYE

Abstract: Abstract

Cited by 8 publications

References 11 publications

Intelligent information extraction to aid science decision making in autonomous space exploration

Intelligent information extraction to aid science decision making in autonomous space exploration

Hiperspektrális távérzékelési módszerek alkalmazhatósága tájvizsgálatokban

Onboard Detection of Jarosite Minerals with Applications to Mars

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