A coarse-to-fine registration strategy for multi-sensor images with large resolution differences

Kai Li*, Yongsheng Zhang, Zhenchao Zhang, Guangling Lai

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
8 Downloads (Pure)

Abstract

Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times.

Original languageEnglish
Article number470
JournalRemote sensing
Volume11
Issue number4
DOIs
Publication statusPublished - 1 Feb 2019

Keywords

  • ACO for continuous domain
  • Diversity of parameters
  • Edge point feature
  • Image registration
  • Normalized mutual information

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