Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition

Authors:
Joseph Ramsey;Paul Gazis;Ted Roush;Peter Spirtes;Clark Glymour
Affiliations:
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA. jdramsey@andrew.cmu.edu;NASA Ames Research Center, Mountain View, CA, USA. pgazis@mail.arc.nasa.gov;NASA Ames Research Center, Mountain View, CA, USA. troush@mail.arc.nasa.gov;Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA&semi/ Institute for Human and Machine Cognition, University of West Florida, USA. ps7z@andrew.cmu.edu;Department of Philosophy and Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA, USA&semi/ Institute for Human and Machine Cognition, University of West Florid ...
Venue:
Data Mining and Knowledge Discovery
Year:
2002

Citing 2
Cited 1

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Data filtering for automatic classification of rocks from reflectance spectra

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

Classification and filtering of spectra: A case study in mineralogy

Intelligent Data Analysis

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Abstract

Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the reliability of several other automated methods adapted to the same purpose. Evidence is given that the procedure can be applied to some other mineral classes as well. Since the procedure is fast with low memory requirements, it is suitable for on-board scientific analysis by orbiters or surface rovers.