A Deep Random Forest Model with Symmetry Analysis for Hyperspectral Image Data Classification Based on Feature Importance

Hyperspectral imagery (HSI), as a core data carrier in remote sensing, plays a crucial role in many fields. Still, it also faces numerous challenges, including the curse of dimensionality, noise interference, and small samples. These problems severely affect the generalization ability and classification accuracy of traditional machine learning and deep learning algorithms. Existing solutions suffer from bottlenecks such as unknown cost matrices and excessive computational overhead. And ensemble learning fails to fully exploit the deep semantic features and feature importance relationships of high-dimensional data. To address these issues, this paper proposes a dual ensemble classification framework (DRF-FI) based on feature importance analysis and a deep random forest. This method integrates feature selection and two-layer ensemble learning. First, it identifies discriminative spectral bands through feature importance quantification. Then, it constructs a balanced training subset through random oversampling. Finally, it integrates four different ensemble strategies. Experimental results on three benchmark hyperspectral datasets demonstrate that DRF-FI exhibits outstanding performance across multiple datasets, particularly excelling in handling highly imbalanced data. Compared to traditional random forests, the proposed method achieves stable improvements in both overall accuracy (OA) and average accuracy (AA). On specific datasets, OA and AA were enhanced by up to 0.84% and 1.24%, respectively. This provides an effective solution to the class imbalance problem in hyperspectral images.