  This study investigates the interpretability, classification, and
segmentation of CT-scan images of rock samples, with a particular focus on the
application of DINOv2 within Geosciences. We compared various segmentation
techniques to evaluate their efficacy, efficiency, and adaptability in
geological image analysis. The methods assessed include the Otsu thresholding
method, clustering techniques (K-means and fuzzy C-means), a supervised machine
learning approach (Random Forest), and deep learning methods (UNet and DINOv2).
We tested these methods using ten binary sandstone datasets and three
multi-class calcite datasets. To begin, we provide a thorough interpretability
analysis of DINOv2's features in the geoscientific context, discussing its
suitability and inherent ability to process CT-scanned rock data. In terms of
classification, the out-of-the-box DINOv2 demonstrates an impressive capability
to perfectly classify rock images, even when the CT scans are out of its
original training set. Regarding segmentation, thresholding and unsupervised
methods, while fast, perform poorly despite image preprocessing, whereas
supervised methods show better results. We underscore the computational demands
of deep learning but highlight its minimal intervention, superior
generalization, and performance without additional image preprocessing.
Additionally, we observe a lack of correlation between a network's depth or the
number of parameters and its performance. Our results show that a LoRA
fine-tuned DINOv2 excels in out-of-distribution segmentation and significantly
outperforms other methods in multi-class segmentation. By systematically
comparing these methods, we identify the most efficient strategy for meticulous
and laborious segmentation tasks. DINOv2 proves advantageous, achieving
segmentations that could be described as "better than ground-truth" against
relatively small training sets.
