A novel multiple kernel boosting method for hyperspectral image classification

Abstract

Multiple kernel learning (MKL) combines multiple base kernels and is becoming more and more popular in machine learning. The choice of kernels is crucial importance for classification performance. In this paper, we propose a new RMKL (RMKBoost) framework for classification in hyperspectral images. The classification is performed in separate two steps. The key boosting strategy is embedded in the first step, which aims to learn an optimally or suboptimally linear combined kernel from the predefined base kernels. Then, the proposed boosting framework generates weak multiple kernel classifiers using a part of the base kernels randomly selected rather than using all base kernels with randomly training samples. Experiments are conducted on the real hyperspectral data set, and the corresponding experimental result shows that RMKBoost algorithm provides the best performances compared with the state-of-the-art kernel methods.