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Lithological identification and classification constitute indispensable facets of geology, resource exploration, and re-lated disciplines. The emergence of hyperspectral remote sensing has ushered in novel perspectives for lithological identification. The utilization of machine learning to extract information from hyperspectral rock images, thereby enabling accurate lithological identification, holds paramount practical significance. Currently, the application of machine learning methods for the classification of hyperspectral rock images lacks a comprehensive exploitation of spatial and spectral information. Therefore, this paper introduces a three-dimensional convolutional residual network structure augmented with an attention mechanism, capable of effectively extracting spatial, spectral, and joint spatial-spectral features from hyperspectral rock images. In this experiment, images of 10 different types of rock samples were collected using a drone equipped with a hyperspectral sensor. The algorithm proposed in this study was applied to classify hyperspectral rock images. Experimental results indicate that, in comparison to traditional machine learning algo-rithms such as SVM and RF, as well as deep learning algorithms like ResNet, 3DCNN, and SSRN, the proposed algorithm exhibits higher accuracy.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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