Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Abstract

Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.

Hyperspectral Inversion of Heavy Metal Copper Content in Corn Leaves Based on DRS–XGBoost

“…PLSR, a traditional statistical method [37], and XGBoost, a powerful machine learning method [38], were both employed in this study as regression analysis techniques that are widely used in the field. A comparative analysis of the PLSR and XGBoost prediction models revealed that the XGBoost model achieved higher prediction accuracy and better robustness, which is consistent with the research findings of Lin et al [39].…”

Improving The Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing The Coupling Effect of Soil Physical Properties on The Spectrum

“…The Newton method is used to solve the extreme value of the loss function, which is expanded to the second order using the Taylor formula. The loss function is optimized with the first-order gradient function and second-order gradient function to reduce model complexity [56]. Simultaneously, the probability of over-fitting is reduced through regularization, significantly improving the model's generalization ability.…”

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

“…The Newton method is used to solve the extreme value of the loss function, which is expanded to the second order using the Taylor formula. The loss function is optimized with the first-order gradient function and second-order gradient function to reduce the model complexity [53]. Simultaneously, the probability for over-fitting is reduced through regularization, significantly improving the model's generalization ability.…”