Abstract. Mars probes send back to Earth enormous amount of data. Automating the analysis of this data and its interpretation represents a challenging test of significant benefit to the domain of planetary science. In this study, we propose combining terrain segmentation and classification to interpret Martian topography data and to identify constituent landforms of the Martian landscape. Our approach uses unsupervised segmentation to divide a landscape into a number of spatially extended but topographically homogeneous objects. Each object is assigned a 12 dimensional feature vector consisting of terrain attributes and neighborhood properties. The objects are classified, based on their feature vectors, into predetermined landform classes. We have applied our technique to the Tisia Valles test site on Mars. Support Vector Machines produced the most accurate results (84.6 % mean accuracy) in the classification of topographic objects. An immediate application of our algorithm lies in the automatic detection and characterization of craters on Mars. 1

Abstract. This paper presents a novel region discovery framework geared towards finding scientifically interesting places in spatial datasets. We view region discovery as a clustering problem in which an externally given fitness function has to be maximized. The framework adapts four representative clustering algorithms, exemplifying prototype-based, gridbased, density-based, and agglomerative clustering algorithms, and then we systematically evaluated the four algorithms in a real-world case study. The task is to find feature-based hotspots where extreme densities of deep ice and shallow ice co-locate on Mars. The results reveal that the density-based algorithm outperforms other algorithms inasmuch as it discovers more regions with higher interestingness, the grid-based algorithm can provide acceptable solutions quickly, while the agglomerative clustering algorithm performs best to identify larger regions of arbitrary shape. Moreover, the results indicate that there are only a few regions on Mars where shallow and deep ground ice co-locate, suggesting that they have been deposited at different geological times.

The Mars Exploration Rover (MER) mission, begun in January 2004, has been extremely successful. However, decision-making for many operation tasks of the current MER mission and the 1997 Mars Pathfinder mission is performed on Earth through a predominantly manual, time-consuming process. Unmanned planetary rover navigation is ideally expected to reduce rover idle time, diminish the need for entering safe-mode, and dynamically handle opportunistic science events without required communication to Earth. Successful automation of rover navigation and localization during the extraterrestrial exploration requires that accurate position and attitude information can be received by a rover and that the rover has the support of simultaneous localization and mapping. An integrated approach with Bundle Adjustment (BA) and Visual Odometry (VO) can efficiently refine the rover position. However, during the MER mission, BA is done manually because of the difficulty in the automation of the cross-site tie points selection. This dissertation proposes an automatic approach to select cross-site tie points from multiple rover sites based on the methods of landmark extraction, landmark modeling, and landmark matching.

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Abstract. An automated crater detection algorithm based on Martian DEM data is developed and applied to the test site located around the Herschel crater. The results are compared to the image-based catalog of Martian craters compiled by Barlow. An algorithm finds many small craters not listed in the Barlow catalog, but it fails to detect heavily degraded craters. A detailed quality assessment of the algorithm is presented. The DEM-based crater detection algorithm offers a relatively simple and ready-to-use tool for identification and characterization of Martian craters large enough to show in the topographic data. Introduction. Impact craters are among the most studied features on Martian surface. Their importance stems from the worth of information that a detailed analysis of their number and morphology can bring forth. Because building manually

Prompted by crater counts as the only available tool for measuring remotely the relative ages of geologic formations on planets, advances in remote sensing have produced a very large database of high resolution planetary images, opening up an opportunity to survey much more numerous small craters improving the spatial and temporal resolution of stratigraphy. Automating the process of crater detection is key to generate comprehensive surveys of smaller craters. Here we discuss two supervised machine learning techniques for crater detection algorithms (CDA): identification of craters from digital elevation models (also known as range images), and identification of craters from panchromatic images. We present applications of both techniques and demonstrate how such automated analysis has produced new knowledge about planet Mars.