Kernel Fused Representation-Based Classifier for Hyperspectral Imagery

Abstract

In this letter, we propose a kernel fused representation-based classifier (KFRC) for hyperspectral images (HSIs), which combines sparse representation (SR) and collaborative representation (CR) into a unified kernel representation-based classification framework. First, we present two individual kernel methods, i.e., kernel SR (KSR) and kernel CR (KCR), which kernelize the representation methods by projecting the samples into a high-dimensional kernel space to improve the samples separability between different classes. Once obtaining the two kernel representation coefficients, KFRC attempts to achieve a balance between KSR and KCR via an adjusting parameter $\theta $ in the kernel residual domain. Subsequently, the class label of each test sample is determined by the minimum residual for each class. Experimental results on two HSIs demonstrate the proposed kernel fused method performs better than the other state-of-the-art representation-based classifiers.