Study on the Decision Tree Model for Carbonate Rock Lithology Identification Based on Hyperspectral Data

Fig. 5. Box plots of particial features. (a) Absorption bandwidth; (b) right shoulder wavelength of the absorption band; (c) the lowest point wavelength of the absorption valley

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Fig. 6. Scatter plot of particial features

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Fig. 7. Classification accuracy of different features at different thresholds. (a) Right shoulder wavelength of the absorption band and the lowest point wavelength of the absorption valley; (b) absorption bandwidth

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Fig. 8. Decision tree model accuracy under different feature combinations

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Fig. 9. Processing flowchart of decision tree

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Fig. 10. Classification accuracy of different features for different test sets. (a) The lowest point wavelength of the absorption valley; (b) right shoulder wavelength of the absorption band; (c) absorption bandwidth

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Table 1. Confusion matrix
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Table 1. Confusion matrix
Actual class Predicted class
Positive Negative
Positive $N_{T P}$ $N_{F N}$
Negative $N_{F P}$ $N_{T N}$

Table 2. Comparison of accuracy among different models
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Table 2. Comparison of accuracy among different models
Model Accuracy Model Accuracy
LR 92.04 NN 93.81
SVM 92.92 LDA 90.27
K-NN 91.34 Proposed 95.57

Table 3. Feature importance under different test set proportions
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Table 3. Feature importance under different test set proportions
Test set proportion λ_m λ₂ W λ₁ D H Q_SAI S ρ_m
0.1 0.26 0.24 0.15 0.10 0.06 0.05 0.05 0.05 0.04
0.2 0.21 0.24 0.17 0.13 0.05 0.06 0.05 0.04 0.05
0.3 0.24 0.22 0.18 0.12 0.06 0.05 0.05 0.04 0.04

Table 4. Confusion matrix by decision tree model
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Table 4. Confusion matrix by decision tree model
Actual Predicted
Dolostone Limestone
Dolostone 77 3
Limestone 2 31

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Yu Huang, Yanlin Shao, Wei Wei, Qihong Zeng. Study on the Decision Tree Model for Carbonate Rock Lithology Identification Based on Hyperspectral Data[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0837007

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