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Region-based image registration for remote sensing imagery.

Azubuike Okorie1, Sokratis Makrogiannis1

  • 1Delaware State University, 1200 N DuPont Hwy, Dover, DE 19901, USA.

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|August 25, 2023
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Summary
This summary is machine-generated.

This study introduces an automatic region-based method for registering remote sensing images. The novel approach uses joint intensity-Fourier descriptors for accurate image matching, outperforming traditional feature-based techniques.

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Area of Science:

  • Geosciences
  • Computer Science
  • Remote Sensing

Background:

  • Accurate registration of remote sensing imagery is crucial for various geospatial applications.
  • Traditional local feature-based methods can be susceptible to errors from feature estimators.

Purpose of the Study:

  • To develop an automatic region-based registration method for remote sensing images.
  • To improve registration accuracy by matching region properties instead of local features.

Main Methods:

  • Automated image segmentation using kernel density estimators, morphological reconstruction, and watershed transform.
  • Calculation of regional Fourier descriptors and standardized regional intensity descriptors.
  • Definition of a joint matching cost based on Euclidean distances for region correspondence.

Main Results:

  • The proposed joint intensity-Fourier descriptor method achieved high accuracy on synthetic and real datasets.
  • Average root-mean-squared error (RMSE) of 0.446 ± 0.359 pixels and 1.152 ± 0.488 pixels on satellite imagery.
  • Demonstrated lower registration error compared to Harris, FAST, SURF, BRISK, and KAZE keypoint descriptors.

Conclusions:

  • The proposed region-based registration method offers high accuracy for remote sensing imagery.
  • The joint intensity-Fourier descriptor approach effectively addresses limitations of local feature estimators.
  • This technique provides a robust and accurate solution for remote sensing image registration.