Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines

Extreme learning machine (ELM) is an efficient learning algorithm for multi-classification and regression. However, original ELM doesn't consider the weight of each sample in training-set, which may cause the accuracy decreasing especially in imbalanced datasets. Even if each training sample is assigned with an extra weight, the problem on how to determinate the weight adaptively still remains. Inspiration by AdaBoost algorithm, we embed the weighted ELM algorithm in AdaBoost framework. In the meanwhile, we incorporate spatial and spectral information in composite kernel for each sample, which has a good performance in hyperspectral image (HSI) classification. By combining composite kernel methods and Adaboost framework with weighted ELM, a novel algorithm, namely AdaBoost composite kernel extreme learning machines denoted as AdaBoost-WCKELM is proposed. Experimental results demonstrate that the proposed method outperforms current state-of-the-art algorithms and derives a good improvement in HSI classification accuracy.