Super-resolution reconstruction algorithm to MODIS remote sensing images

Huanfeng Shen, Kwok Po NG*, Pingxiang Li, Liangpei Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)90-100
Number of pages11
JournalComputer Journal
Volume52
Issue number1
DOIs
Publication statusPublished - Jan 2009

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • Huber prior
  • L norm data fidelity
  • MODIS images
  • Outliers
  • Super-resolution

Fingerprint

Dive into the research topics of 'Super-resolution reconstruction algorithm to MODIS remote sensing images'. Together they form a unique fingerprint.

Cite this