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Tsinghua Science and Technology 2018, 23(3): 315-322 https://doi.org/10.26599/TST.2018.9010088
Open Access | Issue | Published: 02 July 2018

Sparse Representation over Shared Coefficients in Multispectral Pansharpening

Show Author's Information Liuqing Chen,  Xiaofeng Zhang,  Hongbing Ma( )
Department of Electronic Engineering and State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China.
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Keywords:
sparse representation, pansharpening, shared coefficients, iteration
Cite this article:
Chen L, Zhang X, Ma H. Sparse Representation over Shared Coefficients in Multispectral Pansharpening. Tsinghua Science and Technology, 2018, 23(3): 315-322. https://doi.org/10.26599/TST.2018.9010088
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Abstract Full text About this article

The pansharpening process is for obtaining an enhanced image with both high spatial and high spectral resolutions by fusing a panchromatic (PAN) image and a low spatial resolution multispectral (MS) image. Sparse Principal Component Analysis (SPCA) method has been proposed as a pansharpening method, which utilizes sparse coefficients and over-complete dictionaries to represent the remote sensing data. However, this method still has some drawbacks, such as the existence of the block effect. In this paper, based on SPCA, we propose the Sparse over Shared Coefficients (SSC), in which patches are extracted with a sliding distance of 1 pixel from a PAN image, and the MS image shares the sparse representation coefficients trained from the PAN image independently. The fused high-resolution MS image is reconstructed by K-SVD algorithm and iterations, and residual compensation is applied when the down-sampling constraint is not satisfied. The simulated experiment results demonstrate that the proposed SSC method outperforms SPCA and improves the overall effectiveness.


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About this article

Sparse Representation over Shared Coefficients in Multispectral Pansharpening

Show Author's information Liuqing Chen, Xiaofeng Zhang, Hongbing Ma( )
Department of Electronic Engineering and State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China.
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

Abstract

The pansharpening process is for obtaining an enhanced image with both high spatial and high spectral resolutions by fusing a panchromatic (PAN) image and a low spatial resolution multispectral (MS) image. Sparse Principal Component Analysis (SPCA) method has been proposed as a pansharpening method, which utilizes sparse coefficients and over-complete dictionaries to represent the remote sensing data. However, this method still has some drawbacks, such as the existence of the block effect. In this paper, based on SPCA, we propose the Sparse over Shared Coefficients (SSC), in which patches are extracted with a sliding distance of 1 pixel from a PAN image, and the MS image shares the sparse representation coefficients trained from the PAN image independently. The fused high-resolution MS image is reconstructed by K-SVD algorithm and iterations, and residual compensation is applied when the down-sampling constraint is not satisfied. The simulated experiment results demonstrate that the proposed SSC method outperforms SPCA and improves the overall effectiveness.

Keywords: sparse representation, pansharpening, shared coefficients, iteration

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Received: 01 October 2017
Accepted: 09 February 2018
Published: 02 July 2018
Issue date: June 2018

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