Abstract
We propose to use an unsupervised automated classification of topographic features on Mars in order to speed up geomorphic and geologic mapping of the planet. We construct a digital topography model (DTM), a multilayer grid that stores various kinds of topographical information for every pixel in a site. The method uses a probabilistic clustering algorithm to assign topographically meaningful labels to all pixels in the DTM. The results are displayed as a thematic map of topography. Resultant topographical features are characterized and compared using statistics of their constituent pixels. We demonstrate the usage of our method by classifying and characterizing the topography of a landscape in the Tisia Valles region on Mars. We discuss extensions and further applications of our method.
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