| Autor: |
Chen, Jiawei, Jiao, Licheng |
| Předmět: |
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| Zdroj: |
International Journal of Remote Sensing; Feb2015, Vol. 36 Issue 3, p734-748, 15p, 1 Diagram, 4 Charts, 5 Graphs |
| Abstrakt: |
Sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) have been proposed for image classification. Sparsity is overemphasized in SRC, but ignored in CRC, which has low representation complexity. In this article, a new local collaborative representation scheme is proposed for the classification of hyperspectral imagery (HSI). First, for a test sample with an unknown label, a few significant atoms are selected based on their contributions in representing the test sample, where the-norm is replaced by the-norm like CRC to reduce the complexity. Next, the test sample is represented by the active atoms. Finally, the class label of the test sample is determined based on the contribution of each class of active atoms. Additionally, in order to improve the robustness of the active atoms, both the test sample and the samples in its neighbourhood as context information are considered in the selection of active atoms and the class determination. Experimental results on three real HSI data sets confirm the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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