  Planetary exploration missions with Mars rovers are complicated, which
generally require elaborated task planning by human experts, from the path to
take to the images to capture. NASA has been using this process to acquire over
22 million images from the planet Mars. In order to improve the degree of
automation and thus efficiency in this process, we propose a system for
planetary rovers to actively search for prominence of prespecified scientific
features in captured images. Scientists can prespecify such search tasks in
natural language and upload them to a rover, on which the deployed system
constantly captions captured images with a deep image captioning network and
compare the auto-generated captions to the prespecified search tasks by certain
metrics so as to prioritize those images for transmission. As a beneficial side
effect, the proposed system can also be deployed to ground-based planetary data
systems as a content-based search engine.
