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)
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.
2015, Vol. 37 
