Abstract In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.
This research was supported by the National Natural Science Foundation of China (Grant No. 61275010, 61077079), the State Key Program of National Natural Science Foundation of Heilongjiang Province of China (No. ZD201216), and the Fundamental Research Funds for the Central Universities (No. HEUCF130820).
Cite this article:
WANG Li-Guo,LIU Dan-Feng,ZHAO Liang. Sparse representation-based color visualization method for hyperspectral imaging[J]. APPLIED GEOPHYSICS, 2013, 10(2): 210-221.
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2013, Vol. 10 
