Abstract
Spatial resolution enhancement of hyperspectral images is one of the key and difficult topics in the field of imaging spectrometry. The redundant dictionary based sparse representation theory is introduced, and a spatial resolution enhancement algorithm is proposed. In this algorithm, a pixel curve instead of a pixel patch is taken as the unit of processing. A pair of low- and high-resolution respective redundant dictionaries are joint trained, with the constraint that a pair of high- and low-resolution corresponded pixel curves can be sparse represented by same coefficients according to the respected dictionaries. In the process of super-resolution restoration, the low-resolution hyperspectral image is first sparse decomposed based on the low-resolution redundant dictionary and then the obtained coefficients are used to reconstruct the corresponding high-resolution image with respect to the high-resolution dictionary. The maximum a posteriori based constrained optimization is performed to further improve the quality of the reconstructed high-frequency information. Experimental results show that the pixel curve based sparse representation is more suitable for a hyperspectral image; the highly spectral correlations are better used for resolution enhancement. In comparison with the traditional bilinear interpolation method and other referenced super-resolution algorithms, the proposed algorithm is superior in both objective and subjective results.
Highlights
Hyperspectral imaging is a major breakthrough in the field of earth observation that occurred at the end of the last century
C is the coefficient matrix of the sparse representation for pixel curves X, and T0 is the subjective assumption of sparsity satisfying T0 < K, where K is the number of atoms in the dictionary
When the high resolution (HR) and low resolution (LR) corresponding redundant dictionary pair is created, for any pixel curve xil in the LR hyperspectral image, its sparse representation coefficients can be obtained through Eq (6)
Summary
Hyperspectral imaging is a major breakthrough in the field of earth observation that occurred at the end of the last century. Super-resolution (SR) restoration,[3,4] a signal processing based resolution enhancement method, provides a new choice to resolve this problem without any hardware costs This technique has been widely investigated for visible images/videos. A pixel curve based sparse method has been proven effective for describing hyperspectral images and has shown good performances in spectral unmixing and classification applications, but few for SR. The sparse representation based SR method[6] is specially investigated for hyperspectral images and a redundant dictionary based hyperspectral image SR restoration algorithm is proposed. When all the curves are SR reconstructed, all the bands of the estimated HR image are optimized by a maximum a prior (MAP) based algorithm to further improve the quality In this algorithm, the hyperspectral images are sparsely decomposed as a whole for each pixel on the spectral dimension.
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