# Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification Source: [Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:03:28.249Z Authors: Soheil Bahrampour, Nasser M. Nasrabadi, Asok Ray, Kenneth W. Jenkins Published: 2015-02-10T21:27:27.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()