Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification
| Autor: | Nasser M. Nasrabadi, Kenneth W. Jenkins, Asok Ray, Soheil Bahrampour |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
FOS: Computer and information sciences
Contextual image classification Pixel business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Hyperspectral imaging Pattern recognition Machine Learning (stat.ML) Sparse approximation Machine Learning (cs.LG) Kernel (linear algebra) Statistics::Machine Learning Computer Science - Learning Kernel method ComputingMethodologies_PATTERNRECOGNITION Kernel (image processing) Discriminative model Statistics - Machine Learning Computer Science::Computer Vision and Pattern Recognition Artificial intelligence business Classifier (UML) |
| Zdroj: | ICASSP |
| Popis: | 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. 5 pages, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2015 |
| Databáze: | OpenAIRE |
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