Interactive Mars Image Content-Based Search with Interpretable Machine
  Learning
Bhavan Vasu Steven Lu Emily Dunkel Kiri L. Wagstaff Kevin Grimes Michael McAuley
The NASA Planetary Data System (PDS) hosts millions of images of planets,
moons, and other bodies collected throughout many missions. The ever-expanding
nature of data and user engagement demands an interpretable content
classification system to support scientific discovery and individual curiosity.
In this paper, we leverage a prototype-based architecture to enable users to
understand and validate the evidence used by a classifier trained on images
from the Mars Science Laboratory (MSL) Curiosity rover mission. In addition to
providing explanations, we investigate the diversity and correctness of
evidence used by the content-based classifier. The work presented in this paper
will be deployed on the PDS Image Atlas, replacing its non-interpretable
counterpart.