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TextureCam : a smart camera for microscale, mesoscale, and deep space applications

Show simple item record Abbey, William Allwood, Abigail Bekker, Dmitriy Bornstein, Benjamin Cabrol, Nathalie A. Castano, Rebecca Chien, Steve A. Doubleday, Joshua Estlin, Tara Foil, Greydon Fuchs, Thomas Howarth, Daniel Ortega, Kevin Thompson, David R. Wagstaff, Kiri L. 2013-11-11T16:24:27Z 2013-11-11T16:24:27Z 2013-03-04
dc.identifier.citation 44th Lunar and Planetary Sciences Conference, Woodlands, Texas, March 18-22, 2013 en_US
dc.identifier.clearanceno 13-0305
dc.description.abstract The TextureCam project is developing a 'smart camera' that can classify geologic surfaces in planetary images. This would allow autonomous spacecraft to collect data opportunisitcally during intervals between communications with Earth, such as during long traverses. Its surface classifications can identify new targets that were not anticipated in advance. The spacecraft might use this information to target these features with high-resolution instruments such as spectrometers nd narrow-field cameras. Classifications could also inform data 'triage' decisions, identifying high value images for prioritized downlink. Finally, the surface classification can serve as compressed maps of image content. Each of these strategies can improve the science data returned at each command cycle and speed reconnaissance during site survey and astrobiology investigation. Our first year of development has completed the image analysis algorithms and validated them in software tests. Here we survey these initial results and explore several application areas relevant to Mars and beyond. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2013. en_US
dc.subject Hyperspectral Infrared Radiometer (HyspIRI) en_US
dc.subject Hyperspectral imagery en_US
dc.title TextureCam : a smart camera for microscale, mesoscale, and deep space applications en_US
dc.type Preprint en_US

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