Work

Hyperspectral Lithological Classification of 81 Rock Types Using Deep Ensemble Learning Algorithms

Year: 2025

Type: article

Abstract: To address overfitting due to limited sample size, and the challenges posed by “Spectral Homogeneity with Material Heterogeneity (SHMH)” and “Material Consistency with Spectral Divergence (MCSD)”—which arise from subtle spectral differences and limited classification accuracy—this study proposes a deep integration model that combines the Adaptive Boosting (AdaBoost) algorithm with a convolutional recurrent neural network (CRNN). The model adopts a dual-branch architecture integrating a 2D-CNN and gated recurrent unit to effectively fuse spatial and spectral features of rock samples, while the integration of the AdaBoost algorithm optimizes performance by enhancing system stability and generalization capability. The experiment used a hyperspectral dataset containing 81 rock samples (46 igneous rocks and 35 metamorphic rocks) and evaluated model performance through five-fold cross-validation. The results showed that the proposed 2D-CRNN-AdaBoost model achieved 92.55% overall accuracy, which was significantly better than that of other comparative models, demonstrating the effectiveness of multimodal feature fusion and ensemble learning strategy.

Source: Minerals

DOI: 10.3390/min15080844

Authors: Shanjuan Xie, Yichun Qiu, Shixian Cao, Wenyuan Wu

Institution: Hangzhou Normal University

Language: English

Topic: Geochemistry and Geologic Mapping

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China