  Automatic terrain recognition in Mars rover images is an important problem
not just for navigation, but for scientists interested in studying rock types,
and by extension, conditions of the ancient Martian paleoclimate and
habitability. Existing approaches to label Martian terrain either involve the
use of non-expert annotators producing taxonomies of limited granularity (e.g.
soil, sand, bedrock, float rock, etc.), or rely on generic class discovery
approaches that tend to produce perceptual classes such as rover parts and
landscape, which are irrelevant to geologic analysis. Expert-labeled datasets
containing granular geological/geomorphological terrain categories are rare or
inaccessible to public, and sometimes require the extraction of relevant
categorical information from complex annotations. In order to facilitate the
creation of a dataset with detailed terrain categories, we present a
self-supervised method that can cluster sedimentary textures in images captured
from the Mast camera onboard the Curiosity rover (Mars Science Laboratory). We
then present a qualitative analysis of these clusters and describe their
geologic significance via the creation of a set of granular terrain categories.
The precision and geologic validation of these automatically discovered
clusters suggest that our methods are promising for the rapid classification of
important geologic features and will therefore facilitate our long-term goal of
producing a large, granular, and publicly available dataset for Mars terrain
recognition.
