Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification

TL;DR

This paper introduces a supervised kernel domain dictionary learning method for hyperspectral image classification that jointly learns dictionaries and classifiers, leveraging structured sparsity priors for improved performance.

Contribution

It proposes a novel task-driven dictionary learning algorithm in the kernel domain that enforces collaboration among neighboring pixels using a joint sparsity prior.

Findings

Efficient classification performance demonstrated on hyperspectral data.

Joint dictionary and classifier learning improves accuracy.

Structured sparsity priors enhance pixel collaboration.

Abstract

Dictionary learning algorithms have been successfully used in both reconstructive and discriminative tasks, where the input signal is represented by a linear combination of a few dictionary atoms. While these methods are usually developed under $\ell_1$ sparsity constrain (prior) in the input domain, recent studies have demonstrated the advantages of sparse representation using structured sparsity priors in the kernel domain. In this paper, we propose a supervised dictionary learning algorithm in the kernel domain for hyperspectral image classification. In the proposed formulation, the dictionary and classifier are obtained jointly for optimal classification performance. The supervised formulation is task-driven and provides learned features from the hyperspectral data that are well suited for the classification task. Moreover, the proposed algorithm uses a joint ($\ell_{12}$) sparsity prior to enforce collaboration among the neighboring pixels. The simulation results illustrate the efficiency of the proposed dictionary learning algorithm.