A Refined Rotation Forest-Based Ensemble Classifier for Lithological Mapping with ZY1-02D Hyperspectral Remote...

Hyperspectral remote sensing data provides distinct advantages for lithological classification in bedrock-exposed areas. Despite the superior performance of ensemble learning methods (e.g., Rotation Forest, ROF) in big data classification, their application in high-dimensional hyperspectral data is restricted by high training costs. To address this limitation and improve classification accuracy, this study proposes an optimized ROF-LightGBM ensemble algorithm integrated with minimum noise fraction (MNF) for rotation matrix construction. Experimental validation was conducted using ZY1-02D hyperspectral data for lithological mapping in the bedrock-exposed Xitieshan area, involving ROF-LightGBM parameter optimization (L × T, bootstrap) and comparative experiments with multiple machine learning models. The results demonstrate the following: under the same number of decision trees (T = 100), the ROF-LightGBM (PCA, L × T = 4 × 25) with optimized base classifier outperforms random forest (RF), LightGBM, and traditional ROF (L = 100) models in classification accuracy, achieving 74.28% accuracy, 6.54% higher than RF and 1.53% higher than LightGBM. More notably, it boasts exceptional efficiency, with a training time of only 4.86 s (nearly 37 times shorter than traditional ROF), while maintaining minimal accuracy loss (an only 1.19% decrease). Additionally, the ROF-LightGBM (MNF) model, which adopts MNF for rotation matrix construction, further enhances performance. Compared with the PCA-based ROF-LightGBM, it achieves an 82.17% classification accuracy (a 7.89% increase) and its kappa coefficient reaches 0.81, fully verifying the model's superiority in accuracy and efficiency.