Multiview-Based Random Rotation Ensemble Pruning for Hyperspectral Image Classification

Ensembles of extreme learning machine (ELM) have been widely used for hyperspectral image classification. The previous studies have shown that the goal of ensemble learning is to train accurate but diverse component classifiers to improve the generalization performance. To approach this goal, this article proposes a novel framework to construct an ELM ensemble model. The proposed framework relies on multiview-based random rotation ensemble pruning (MVRR-EP) and has several novel features. First, to ensure that the subsets of spectral bands can sufficiently learn the target concept, the spectral bands are divided into multiviews by using correlation analysis. Second, random rotation, a new approach of space transformation, is introduced to transform each view into multiple coordinate spaces, which makes the component classifiers trained on the transformed spaces have great diversity. Third, an accuracy guided ensemble pruning strategy is designed for pruning the component classifiers with low complementarity, and consequently, the remaining component classifiers with high complementarity are combined to a construct ensemble classifier. These techniques guarantee that the component classifiers used to construct an ensemble classifier are accurate but diverse, which ultimately improves the performance of the ensemble classifier. To demonstrate the effectiveness of the proposed MVRR-EP, extensive experiments were carried out on four hyperspectral data sets. Experimental results verify that compared with other methods, the proposed method provides competitive results.