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The Computer Journal Advance Access originally published online on June 24, 2007
The Computer Journal 2009 52(1):90-100; doi:10.1093/comjnl/bxm028
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© The Author 2007. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images

Huanfeng Shen1, Michael K. Ng2,*, Pingxiang Li1 and Liangpei Zhang1

1 The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China
2 Centre for Mathematical Imaging and Vision, Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

* Corresponding author: mng{at}math.hkbu.edu.hk

Received 12 May 2006; revised 23 April 2007

In this paper, we propose a super-resolution image reconstruction algorithm to moderate-resolution imaging spectroradiometer (MODIS) remote sensing images. This algorithm consists of two parts: registration and reconstruction. In the registration part, a truncated quadratic cost function is used to exclude the outlier pixels, which strongly deviate from the registration model. Accurate photometric and geometric registration parameters can be obtained simultaneously. In the reconstruction part, the L1 norm data fidelity term is chosen to reduce the effects of inevitable registration error, and a Huber prior is used as regularization to preserve sharp edges in the reconstructed image. In this process, the outliers are excluded again to enhance the robustness of the algorithm. The proposed algorithm has been tested using real MODIS band-4 images, which were captured in different dates. The experimental results and comparative analyses verify the effectiveness of this algorithm.

Key Words: super-resolution • MODIS images • outliers • L1 norm data fidelity • Huber prior


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