DINOv2 Rocks Geological Image Analysis: Classification, Segmentation, and Interpretability

• URL: http://arxiv.org/abs/2407.18100v1
• Date: Thu, 25 Jul 2024 15:03:36 GMT
• Title: DINOv2 Rocks Geological Image Analysis: Classification, Segmentation, and Interpretability
• Authors: Florent Brondolo, Samuel Beaussant,
• Abstract summary: This study investigates the interpretability, classification, and segmentation of CT-scan images of rock samples. We compare various segmentation techniques to evaluate their efficacy, efficiency, and adaptability in geological image analysis. We find that a LoRA fine-tuned DINOv2 excels in out-of-distribution segmentation and significantly outperforms other methods in multi-class segmentation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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