An Adaptive Multi-Splitting Multivariate Decision Tree for Multi-Class Classification Applied on High-Resolution and Hyperspectral Remote Sensing Images

In remote sensing data analysis, multi-class classification plays a critical role in distinguishing multiple pattern types, and decision trees are particularly well-suited for this task due to their computational efficiency and interpretability. Existing decision tree approaches often suffer from suboptimal handling of multi-class problems, vulnerability to class imbalance, or degraded generalization ability. To address these limitations, this paper proposes an adaptive multi-splitting multivariate decision tree designed explicitly for multi-class classification. The core of our approach is an effective homogeneity cluster discovery strategy that directly optimizes a multi-class sample separation criterion at each node, eliminating dependency on decomposition schemes and mitigating the associated class imbalance problem. This is coupled with an adaptive splitting mechanism that dynamically chooses between multi-splitting and bi-splitting at each node based on local data geometry and class labels. Experimental evaluations on a synthetic multi-class dataset, noisy remote sensing RGB scene image datasets demonstrate that the proposed model outperforms existing decision tree methods in classification accuracy and F1 score with compact tree structures and maintains competitive computational efficiency. In the remote sensing hyperspectral image classification application, the proposed model improves overall accuracy by up to 6.99% over the baseline deep learning model on the highly class-imbalanced Indian Pines dataset. This work provides a flexible and effective multivariate decision tree classifier, which can improve multi-class classification performance while keeping high efficiency.