Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification
This work addresses hyperspectral image classification, which is important for remote sensing applications, but it is incremental as it builds on existing kernel and structured sparsity methods.
The authors tackled hyperspectral image classification by proposing a supervised dictionary learning algorithm in the kernel domain that jointly optimizes the dictionary and classifier for improved performance, achieving efficient results as demonstrated in simulations.
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.