Anti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification

Abstract

Achieving high classification accuracy is vital in reliable information extraction from images. Single classifiers and existing ensemble methods suffer from data dimensionality, insufficient ground truth information and lack in defining optimal feature selection. This article presents a novel idea for constructing component classifiers that boost random subspace ensemble method in improving its classification performance. It is achieved through sub-optimal training of component classifiers through interference in training process during validation error evaluation. The new approach allows to enforce different class errors among component classifiers, besides improving individual class accuracy. This article demonstrates effectiveness of the anti-cross validation approach using three classical hyperspectral Image (HSI) datasets with significant improvement in classification accuracies from 3 to 10% with the proposed approach.

Keywords:

• Neural network ensemble
• cross validation
• random subspace
• ensemble diversity
• hyperspectral classification

Acknowledgements

The authors acknowledge the support from DST NISA, India, to the 1st author to carry out this research.

Disclosure statement

No potential conflict of interest was reported by the authors.