Hyperspectral sensing data analysis based on quasiconformal mapping-based multiple kernels learning machine

Hyperspectral remote sensing has a strong ability of object information expression, so it provides better support for object classification. Many methods are proposed for the hyperspectral data classification. The spectrum classification is a classical nonlinear problem, and a kernel-based machine is feasible to classify the spectrum data. In the nonlinear kernel-based space, the spectrum data are more discriminative. The kernel functions determine the data distribution in the feature space. In this paper, we propose the quasiconformal multiple kernels-based machine learning for the hyperspectral data classification. In the framework, the structure of hyperspectral data is adaptively adjusted for classification. The multiple kernels extract the multiple features of hyperspectral data for classification. Multiple features-based machine learning exhibits a great potential on the classification of hyperspectral data. Two public datasets, India Pines dataset and Pavia University dataset, are used to test the proposed algorithm. Experimental results demonstrate that the proposed quasiconformal multiple kernels-based hyperspectral data classification method can show competitive performance.