Melissa Bunte

Dynamic Landmarking for Surface Feature Identification and Change Detection

Given the large volume of images being sent back from remote spacecraft, there is a need for automated analysis techniques that can quickly identify interesting features in those images. Feature identification in individual images and automated change detection in multiple images of the same target are valuable for scientific studies and can inform subsequent target selection. We introduce a new approach to orbital image analysis called dynamic landmarking. It focuses on the identification and comparison of visually salient features in images. We have evaluated this approach on images collected by five Mars orbiters. These evaluations were motivated by three scientific goals: to study fresh impact craters, dust devil tracks, and dark slope streaks on Mars. In the process we also detected a different kind of surface change that may indicate seasonally exposed bedforms. These experiences also point the way to how this approach could be used in an onboard setting to analyze and prioritize data as it is collected.

Autonomous detection of volcanic plumes on outer planetary bodies

We experimentally evaluated the efficacy of various autonomous supervised classification techniques for detecting transient geophysical phenomena. We demonstrated methods of detecting volcanic plumes on the planetary satellites Io and Enceladus using spacecraft images from the Voyager, Galileo, New Horizons, and Cassini missions. We successfully detected 73-95% of known plumes in images from all four mission datasets.Additionally, we showed that the same techniques are applicable to differentiating geologic features, such as plumes and mountains, which exhibit similar appearances in images.