Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification

Autor: Zhen-tao Qin, Wu-nian Yang, Ru Yang, Xiang-yu Zhao, Teng-jiao Yang
Jazyk: angličtina
Rok vydání: 2015
Předmět:
Zdroj: Journal of Spectroscopy, Vol 2015 (2015)
Druh dokumentu: article
ISSN: 2314-4920
2314-4939
DOI: 10.1155/2015/678765
Popis: This paper presents a new, dictionary-based method for hyperspectral image classification, which incorporates both spectral and contextual characteristics of a sample clustered to obtain a dictionary of each pixel. The resulting pixels display a common sparsity pattern in identical clustered groups. We calculated the image’s sparse coefficients using the dictionary approach, which generated the sparse representation features of the remote sensing images. The sparse coefficients are then used to classify the hyperspectral images via a linear SVM. Experiments show that our proposed method of dictionary-based, clustered sparse coefficients can create better representations of hyperspectral images, with a greater overall accuracy and a Kappa coefficient.
Databáze: Directory of Open Access Journals
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