Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images

Panambur, Tejas; Chakraborty, Deep; Meyer, Melissa J.; Milliken, R. E.; Learned-Miller, Erik; Parente, M.

doi:10.1109/cvprw56347.2022.00138

Cited by 11 publications

(2 citation statements)

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“…MER [5], MSL [10], HiRISE [16], etc. These datasets have been used to train classifiers to detect classes of interest [18,24]. Development of annotated datasets allowed for deployment of multilabel Convolutional Neural Networks (CNNs) for classification of both science and engineering tasks for individual missions [15,23] which showed performance improvements over Support Vector Machine (SVM) classifiers.…”

Section: Planetary Computer Visionmentioning

confidence: 99%

“…Self-supervised Learning Self-supervised and semi-supervised learning can potentially alleviate the significant effort used to annotate rover images in the AI4Mars dataset. Self-supervised networks have aided in the process of annotating images by developing clusters of relevant terrain classes utilizing unlabeled images [18]. Contrastive learning has emerged as an important self-supervised learning technique where a network is trained on unlabeled data to maximize agreement between randomly augmented views of the same image and minimize agreement between those of different images [3,4].…”

Section: Planetary Computer Visionmentioning

confidence: 99%

See 1 more Smart Citation

Mixed-domain Training Improves Multi-Mission Terrain Segmentation

Vincent¹,

Yepremyan²,

Chen³

et al. 2022

Preprint

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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 mixeddomain 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.

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