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Hybrid µCT-FMT imaging and image analysis

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A hybrid local-global feature attention network for thin section rock image classification.

Peiyang Wei^1,2,3, Changyuan Fan⁴, Xiwen Yang⁵

¹School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China.

Scientific Reports

|January 28, 2026

Summary

HFANet, a new deep learning model, accurately classifies rock thin-section images by combining local textures and global context. This advancement improves geological surveys and resource exploration through enhanced lithological classification.

Keywords:

Attention mechanisms Cross-attention Ensemble learning Geological image analysis HFANet Interpretability Local–global feature fusion Rock thin-section classification

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Area of Science:

Geoscience
Computer Science
Artificial Intelligence

Background:

Accurate classification of rock thin-section images is vital for geological applications.
Current deep learning models face challenges in balancing local texture and global context for fine-grained rock recognition.

Purpose of the Study:

To develop a novel hybrid network, HFANet, for improved thin-section rock image classification.
To enhance the integration of local and global features for more accurate lithological classification.

Main Methods:

Proposed HFANet, a hybrid network integrating DenseNet (local) and Swin Transformer (global) branches.
Implemented multi-head self-attention and bidirectional cross-attention for feature interaction and guidance.
Utilized an ensemble classification framework with adaptive fusion and multimodal fusion of handcrafted geological features.

Main Results:

HFANet achieved superior performance compared to state-of-the-art models on rock thin-section datasets.
Reached up to 99.03% accuracy and perfect AUC/AUPR scores on the sedimentary subset.
Ablation studies and interpretability analyses validated the effectiveness of model components and focus on geological features.

Conclusions:

HFANet effectively balances local and global features for accurate lithological classification.
The model shows significant potential for advancing intelligent geoscientific image analysis.
Demonstrated practical applications in automated petrographic analysis and resource exploration.