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Creation and testing of an artificial neural network based carbonate detector for Mars rovers

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dc.contributor.author Bornstein, Benjamin
dc.contributor.author Castano, Rebecca
dc.contributor.author Gilmore, Martha S.
dc.contributor.author Merrill, Matthew
dc.contributor.author Greenwood, James P.
dc.date.accessioned 2005-08-24T21:18:47Z
dc.date.available 2005-08-24T21:18:47Z
dc.date.issued 2005-03-07
dc.identifier.citation IEEE Aerospace Conference, Big Sky, MT, March 7, 2005 en
dc.identifier.clearanceno IEEE Aerospace Conference, Big Sky, MT, March 7, 2005
dc.identifier.clearanceno IEEE Aerospace Conference, Big Sky, MT, March 7, 2005
dc.identifier.clearanceno 05-0634
dc.identifier.uri http://hdl.handle.net/2014/37447
dc.description.abstract We have developed an artificial neural network (ANN) based carbonate detector capable of running on current and future rover hardware. The detector can identify calcite in visible/NIR (350–2500 nm) spectra of both laboratory specimens covered by ferric dust and rocks in Mars analogue field environments. The ANN was trained using the Backpropagation algorithm with sigmoid activation neurons. For the training dataset, we chose nine carbonate and eight non-carbonate representative mineral spectra from the USGS spectral library. Using these spectra as seeds, we generated 10,000 variants with up to 2% Gaussian noise in each reflectance measurement. We cross-validated several ANN architectures, training on 9,900 spectra and testing on the remaining 100. The best performing ANN correctly detected, with perfect accuracy, the presence (or absence) of carbonate in spectral data taken on field samples from the Mojave desert and clean, pure marbles from CT. Sensitivity experiments with JSC Mars-1 simulant dust suggest the carbonate detector would perform well in aeolian Martian environments. en
dc.description.sponsorship NASA en
dc.format.extent 313710 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2005 en
dc.subject neural networks en
dc.subject carbonates en
dc.title Creation and testing of an artificial neural network based carbonate detector for Mars rovers en
dc.type Preprint en


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