Linear Spectral Mixture Analysis via Multiple-Kernel Learning for Hyperspectral Image Classification

Linear spectral mixture analysis (LSMA) has received wide interests for spectral unmixing in the remote sensing community. This paper introduces a framework called multiple-kernel learning-based spectral mixture analysis (MKL-SMA) that integrates a newly proposed MKL method into the training process of LSMA. MKL-SMA allows us to adopt a set of nonlinear basis kernels to better characterize the data so that it can enrich the discriminant capability in classification. Because a single kernel is often insufficient to well present all the data characteristics, MKL-SMA has the advantage of providing a broader range of representation flexibilities; it also eases the kernel selection process because the kernel combination parameters can be learned automatically. Unlike most MKL approaches where complex nonlinear optimization problems are involved in their training process, we derived a closed-form solution of the kernel combination parameters in MKL-SMA. Our method is thus efficient for training and easy to implement. The usefulness of MKL-SMA is demonstrated by conducting real hyperspectral image experiments for performance evaluation. Promising results manifest the effectiveness of the proposed MKL-SMA.