Fusion of diverse features and kernels using LP-norm based multiple kernel learning in hyperspectral image processing

Abstract

Multiple kernel learning (MKL) is an elegant tool for heterogeneous fusion. In support vector machine (SVM) based classification, MK is a homogenization transform and it provides flexibility in searching for high-quality linearly separable solutions in the reproducing kernel Hilbert space (RKHS). However, performance often depends on input and kernel diversity. Herein, we explore a new way to extract diverse features from hyperspectral imagery using different proximity measures and band grouping. The output is fed to ℓp-norm MKL for feature-level fusion, where larger p's are preferred for diverse vs sparse solutions. Preliminary results on benchmark data indicates that ℓp-norm MKSVM of diverse features and kernels leads to noticeable performance gain.