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21 November 2002 Applications of Machine Learning Techniques in Digital Processing of Images of the Martian Surface
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Abstract
NASA spacecraft have now returned many thousands of images of the surface of Mars. It is no longer practical to analyze such a large dataset by hand, while the development of handwritten feature extraction tools is expensive and laborious. This project investigates the application of machine learning techniques to problems of feature extraction and digital image processing within the Mars dataset. The Los Alamos GENIE machine learning software system uses a genetic algorithm to assemble feature extraction tools from low-level image operators. Each generated tool is evaluated against training data provided by the user. The best tools in each generation are allowed to "reproduce" to produce the next generation, and the population of tools evolves until it converges to a solution or reaches a level of performance specified by the user. Craters are one of the most scientifically interesting and most numerous features on Mars, and present a wide range of shapes at many spatial scales. We now describe results on development of crater finder algorithms using voting sets of simple classifiers evolved by a machine learning/genetic programming system (the Los Alamos GENIE software).
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Catherine S. Plesko, Steven P. Brumby, John C. Armstrong, Elliot A. Ginder, and Conway B. Leovy "Applications of Machine Learning Techniques in Digital Processing of Images of the Martian Surface", Proc. SPIE 4790, Applications of Digital Image Processing XXV, (21 November 2002); https://doi.org/10.1117/12.453861
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