  Planetary rover missions must utilize machine learning-based perception to
continue extra-terrestrial exploration with little to no human presence.
Martian terrain segmentation has been critical for rover navigation and hazard
avoidance to perform further exploratory tasks, e.g. soil sample collection and
searching for organic compounds. Current Martian terrain segmentation models
require a large amount of labeled data to achieve acceptable performance, and
also require retraining for deployment across different domains, i.e. different
rover missions, or different tasks, i.e. geological identification and
navigation. This research proposes a semi-supervised learning approach that
leverages unsupervised contrastive pretraining of a backbone for a
multi-mission semantic segmentation for Martian surfaces. This model will
expand upon the current Martian segmentation capabilities by being able to
deploy across different Martian rover missions for terrain navigation, by
utilizing a mixed-domain training set that ensures feature diversity.
Evaluation results of using average pixel accuracy show that a semi-supervised
mixed-domain approach improves accuracy compared to single domain training and
supervised training by reaching an accuracy of 97% for the Mars Science
Laboratory's Curiosity Rover and 79.6% for the Mars 2020 Perseverance Rover.
Further, providing different weighting methods to loss functions improved the
models correct predictions for minority or rare classes by over 30% using the
recall metric compared to standard cross-entropy loss. These results can inform
future multi-mission and multi-task semantic segmentation for rover missions in
a data-efficient manner.
