The goal of this project is to design and develop a statistical-learning tool (STL) for classification and characterization of topographical features on Mars. Major tools for studying the Martian surface are geomorphic mapping and geologic mapping. The standard approach to perform these mappings is through a manual interpretation of images. This laborious approach severely limits the number of Martian sites amenable to study. The STL automates geomorphic mapping and expedites geologic mapping. Thus, it enables fast and quantitative characterization of large sections of the Martian surface.
The SLT uses digital topography instead of images to characterize Martian sites. Different topographical variables are fused into a multi-layer data structure. Each pixel in a site carries an array of local and regional topographic information. The automatic recognition and classification of topographic features is performed at the pixel level. This enables the quantitative characterization and comparison of different topographic formations based on statistics of their constituent pixels. The results can be conveniently visualized by means of thematic maps of topography. The capacity of the SLT can be extended by adding other data types (multispectral images) and by applying it to other planetary surfaces.
This methodology has a potential to become a powerful investigative tool with a wide range of applications. To facilitate its adoption by the research community the code that implements the SLT and its documentation will be put in the public domain. The results of this work will be disseminated through new courses, seminar talks, and collaborations with other institutes.
