Identification method of volcanic rock slices based on a deep residual shrinkage network

Jianbang Wang; Linfu Xue; Xin Gao

doi:10.1117/12.2668168

23 February 2023 Identification method of volcanic rock slices based on a deep residual shrinkage network

Jianbang Wang, Linfu Xue, Xin Gao

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 125511M (2023) https://doi.org/10.1117/12.2668168
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

The identification of rock slices is the basis of geological research. However, due to the complex mineral composition and structure of volcanic rocks, microscopic analysis is difficult and requires a lot of time for professionals to complete. This paper uses deep learning technology, which has emerged in recent years, to explore a new method of intelligent rock slice identification to achieve automatic identification of volcanic rock slices. The deep residual shrinkage neural network model is used to study the intelligent recognition of volcanic rock slice images. The study investigated 11 basic types of volcanic rock and collected 12,000 high-definition images of rock slices using an electron polarized light microscope. Each image was processed via histogram equalization and image sharpening; data were expanded by random cropping; and the expanded image data were used as a training set to train the network. When the number of model layers reached 50 layers, the best accuracy rate was achieved. After a series of optimizations and improvements of the network model type, the accuracy rate of the test set classification results exceeded 92%.

Citation Download Citation

Jianbang Wang, Linfu Xue, and Xin Gao "Identification method of volcanic rock slices based on a deep residual shrinkage network", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 125511M (23 February 2023); https://doi.org/10.1117/12.2668168

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KEYWORDS

Image processing

Data modeling

Shrinkage

Neural networks

Image enhancement

Histograms

Minerals

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