HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing
Autor: | Aldea, Victor Stefan |
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Rok vydání: | 2014 |
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Druh dokumentu: | Working Paper |
Popis: | Sparse regression methods have been proven effective in a wide range of signal processing problems such as image compression, speech coding, channel equalization, linear regression and classification. In this paper a new convex method of hyperspectral image classification is developed based on the sparse unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization term is introduced. To further enhance class separability, the algorithm is kernelized using an RBF kernel and the final results are improved by a combination of spatial pre and post-processing operations. It is shown that the proposed method is competitive with state of the art algorithms such as SVM-CK, KSOMP-CK and KSSP-CK. Comment: v3: 11 pages, 2 Figures, 10 Tables. Updated the results for the Indian Pines image; added the results for the Pavia University image |
Databáze: | arXiv |
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