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Frontiers of Earth Science

, Volume 12, Issue 4, pp 779–790 | Cite as

Multi-sensor image registration by combining local self-similarity matching and mutual information

  • Xiaoping Liu
  • Shuli Chen
  • Li ZhuoEmail author
  • Jun Li
  • Kangning Huang
Research Article
  • 30 Downloads

Abstract

Automatic multi-sensor image registration is a challenging task in remote sensing. Conventional image registration algorithms may not be applicable when common underlying visual features are not distinct. In this paper, we propose a novel image registration approach that integrates local self-similarity (LSS) and mutual information (MI) for multi-sensor images with rigid/nonrigid radiometric and geometric distortions. LSS is a well-performing descriptor that captures common, local internal layout features for multi-sensor images, whereas MI focuses on global intensity relationships. First, potential control points are identified by using the Harris algorithm and screened based on the self-similarity of their local surrounding internal layouts. Second, a Bayesian probabilistic model for matching the ensemble of the LSS features is introduced. Third, a particle swarm optimization (PSO) algorithm is adopted to optimize the point and region correspondences for maximum self-similarity and MI and, ultimately, a robust mapping function. The proposed approach is compared with several conventional image registration algorithms that are based on the sum of squared differences (SSD), scale-invariant feature transforms (SIFT), and speeded-up robust features (SURF) through the experimental registration of pairs of Landsat TM, SPOT, and RADARSAT SAR images. The results demonstrate that the proposed approach is efficient and accurate.

Keywords

automatic registration multi-sensor images local self-similarity mutual information particle swarm optimization 

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Notes

Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant No. 41371499) and the Natural Science Foundation of Guangdong Province (No. 2015A030313505).

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiaoping Liu
    • 1
  • Shuli Chen
    • 1
  • Li Zhuo
    • 1
    Email author
  • Jun Li
    • 1
  • Kangning Huang
    • 2
  1. 1.Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and PlanningSun Yat-Sen UniversityGuangzhouChina
  2. 2.School of Forestry and Environmental StudiesYale UniversityNew HavenUSA

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