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| Hyperspectral Data Compression Based on Sparse Representation |
| Wu Qian Zhang Rong Xu Da-wei |
(Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China)
(Key Laboratory of Electromagnetic Space Information, Chinese Academy of Science, Hefei 230027, China) |
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Abstract How to reduce the storage and transmission cost of mass hyperspectral data is concerned with growing interest. This paper proposes a hyperspectral data compression algorithm using sparse representation. First, a training sample set is constructed with a band selection algorithm, and then all hyperspectral bands are coded sparsely using a basis function dictionary learned from the training set. Finally, the position indices and values of the non-zero elements are entropy coded to finish the compression. Experimental results reveal that the proposal algorithm achieves better nonlinear approximation performance than 3D-DWT and outperforms 3D-SPIHT. Besides, the algorithm has better performance in spectral information preservation.
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Received: 19 February 2014
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Corresponding Authors:
Zhang Rong
E-mail: zrong@ustc.edu.cn
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2015, Vol. 37 
