Joint Adaboost and multifeature based ensemble for hyperspectral image classification

The paper presents a novel ensemble system which unites Adaboost with multifeature to increase diversity among individual classifiers. Adaboost gives rise to convenience for hyperspectral data classification. To improve the method further, we propose joint Adaboost and multifeature based ensemble (JAME), which assigns different multifeature sets to individual classifiers in Adaboost. Diverse spectral and spatial feature sets are integrated to form multifeature sets. As a result, compared with Adaboost the method has increased the diversity of ensemble system, and better overall accuracies are present. Experiments on hyperspectral data sets reveal that the proposed JAME obtains sound performances comparing with original Adaboost and single classifier.