An innovative multi-kernel learning algorithm for hyperspectral classification.

Many studies show that support vector machine (SVM) techniques have gotten superior performance in hyperspectral classification. SVM classifier’s improvement was fulfilled by the combination of multiple kernel tricks. Multi-kernel learning (MKL) algorithms have been used in hyperspectral processing. Combining SVM and MKL, we propose a novel method for hyperspectral classification. It adopts a boost algorithm rather than the global optimization method to search for the optimal combination of kernel SVMs. Experiments were carried out on several reflectance spectra of plankton and two hyperspectral remote sensing images to verify the approach. It turns out that the proposed method achieved better classification performance than some conventional algorithms.