Autonomous science object recognition allows a spacecraft to identify targets of interest and generate observation requests. This achieves two to three orders of magnitude reduction in downlink bandwidth and enables a principal investigator to prioritize observation requests. As a result the science return of the mission per dollar invested is maximized. Also, autonomous and adaptive object recognizers enable reduction in software design and development cost. Its capabilities are used for producing trainable and adaptive recognizers in real-time.
Scientists indicate targets of interest by pointing at examples in the available imagery.
JARtool automatically learns features and produces recognizers which are uploaded on the spacecraft. Recognizers detect targets of interest in the newly collected data, and only areas of interest are down linked back to Earth. On the scientist's request, multiple shots of the same area of interest are taken. Potentially, super-resolution images can be constructed from multiple images of the same area.