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A Variational Pansharpening Method Based on Gradient Sparse Representation
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DOI：10.1109/LSP.2020.3007325.png)
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Abstract
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By exploiting the gradient similarity between multispectral (MS) and panchromatic (PAN) images, a variational pansharpening method based on gradient sparse representation is proposed, based on the observation that the gradients of corresponding MS and PAN images with different resolutions have the similar sparse coefficients under certain specific dictionaries. By adding a data fidelity term to preserve the spectral information, an optimization model is constructed as a minimization problem of an energy function. The problem can be solved by the gradient descent method efficiently. Experiments on different satellite data reveal that the proposed method outperforms the state-of-the-art methods in terms of visual effect and objective quality analysis.
Keywords:
Pansharpening
variational model
gradient sparse representation
remote sensing
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