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A contour-based approach to multisensor image registration.

H Li1, B S Manjunath, S K Mitra

  • 1Dept. of Electr. and Comput. Eng., California Univ., Santa Barbara, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces two automatic contour-based methods for multispectral and multisenor image registration. These novel techniques accurately align images by matching region boundaries, outperforming manual methods.

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Image Processing

Background:

  • Image registration aligns images of the same scene, crucial for analysis.
  • Multispectral/multisensor image registration is challenging due to differing gray-level characteristics.
  • Contour information (region boundaries) is often preserved across different image types.

Purpose of the Study:

  • To develop robust and efficient automatic contour-based methods for multispectral/multisensor image registration.
  • To address the limitations of traditional area correlation techniques in multisensor scenarios.
  • To improve the accuracy and reduce the computational cost of image registration.

Main Methods:

  • A contour matching algorithm using chain-code correlation and invariant moments for well-preserved contour data (e.g., Landsat, Spot).

Related Experiment Videos

  • An elastic contour matching scheme employing active contour models for registering optical and Synthetic Aperture Radar (SAR) images.
  • Utilizing contours from one image as initial conditions for active contour model fitting in the other.

    • Main Results:

    • Both methods demonstrated automatic and computationally efficient image registration.
    • The algorithms proved robust across various image data types.
    • Performance surpassed manual registration, indicated by a lower root mean square error at control points.

    Conclusions:

    • Contour-based matching is a viable and effective strategy for multispectral/multisensor image registration.
    • The proposed methods offer significant improvements in accuracy and efficiency over existing techniques.
    • These algorithms provide a robust solution for aligning diverse remote sensing imagery.