Department of Mathematics
Super-resolution reconstruction algorithm to MODIS remote sensing images
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. © The Author 2007. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
Huber prior, L1 norm data fidelity, MODIS images, Outliers, Super-resolution
Source Publication Title
Oxford University Press
Link to Publisher's Edition
Shen, H., Ng, M., Li, P., & Zhang, L. (2009). Super-resolution reconstruction algorithm to MODIS remote sensing images. Computer Journal, 52 (1), 90-100. https://doi.org/10.1093/comjnl/bxm028