Hyperspectral image classification with multiple kernel Boosting algorithm

Multiple kernel learning (MKL) is becoming more and more popular in machine learning. Traditional MKL methods usually learn the optimal combinations of both kernels and classifiers as the optimization task which is difficult to be solved. In this paper, we study a Boosting framework of MKL for classification in hyperspectral images. The multiple kernel Boosting (MKBoost) is proposed to solve the MKL problem, which apply the idea of Boosting to the multiple kernel classifiers based on the SVM. Experiments are conducted on different real hyperspectral data sets, and the corresponding experimental results show that MKBoost algorithm provides the best performances compared with the state-of-the-art kernel methods.