  Planetary rover systems need to perform terrain segmentation to identify
drivable areas as well as identify specific types of soil for sample
collection. The latest Martian terrain segmentation methods rely on supervised
learning which is very data hungry and difficult to train where only a small
number of labeled samples are available. Moreover, the semantic classes are
defined differently for different applications (e.g., rover traversal vs.
geological) and as a result the network has to be trained from scratch each
time, which is an inefficient use of resources. This research proposes a
semi-supervised learning framework for Mars terrain segmentation where a deep
segmentation network trained in an unsupervised manner on unlabeled images is
transferred to the task of terrain segmentation trained on few labeled images.
The network incorporates a backbone module which is trained using a contrastive
loss function and an output atrous convolution module which is trained using a
pixel-wise cross-entropy loss function. Evaluation results using the metric of
segmentation accuracy show that the proposed method with contrastive
pretraining outperforms plain supervised learning by 2%-10%. Moreover, the
proposed model is able to achieve a segmentation accuracy of 91.1% using only
161 training images (1% of the original dataset) compared to 81.9% with plain
supervised learning.
