Automated Image Registration (AIR) of MTI Imagery

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This paper describes an algorithm for the registration of imagery collected by the Multispectral Thermal Imager (MTI). The Automated Image Registration (AIR) algorithm is entirely image-based and is implemented in an automated fashion, which avoids any requirement for human interaction. The AIR method differs from the 'direct georeferencing' method used to create our standard coregistered product since explicit information about the satellite's trajectory and the sensor geometry are not required. The AIR method makes use of a maximum cross-correlation (MCC) algorithm, which is applied locally about numerous points within any two images being compared. The MCC method is used to ... continued below

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12 p.

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Pope, P. A. (Paul A.) & Theiler, J. P. (James P.) January 1, 2003.

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Description

This paper describes an algorithm for the registration of imagery collected by the Multispectral Thermal Imager (MTI). The Automated Image Registration (AIR) algorithm is entirely image-based and is implemented in an automated fashion, which avoids any requirement for human interaction. The AIR method differs from the 'direct georeferencing' method used to create our standard coregistered product since explicit information about the satellite's trajectory and the sensor geometry are not required. The AIR method makes use of a maximum cross-correlation (MCC) algorithm, which is applied locally about numerous points within any two images being compared. The MCC method is used to determine the row and column translations required to register the bands of imagery collected by a single SCA (band-to-band registration), and the raw and column translations required to regisler the imagery collected by the three SCAs for any individual band (SCA-to-SCA registration). Of particular note is the use of reciprocity and a weighted least squares approach to obtaining the band-to-band registration shifts. Reciprocity is enforced by using the MCC method to determine the row and column translations between all pair-wise combinations of bands. This information is then used in a weighted least squares approach to determine the optimum shift values between an arbitrarily selected reference band and the other 15 bands. The individual steps of the AIR methodology, and the results of registering MTI imagery through use of this algorithm, are described.

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12 p.

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  • Submitted to the Proceedings of SPIE Aerosense 2003, Vol. 5093, April 21-25, Orlando, Florida

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  • Report No.: LA-UR-03-2254
  • Grant Number: none
  • Office of Scientific & Technical Information Report Number: 976590
  • Archival Resource Key: ark:/67531/metadc933932

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • January 1, 2003

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 5:25 p.m.

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Pope, P. A. (Paul A.) & Theiler, J. P. (James P.). Automated Image Registration (AIR) of MTI Imagery, article, January 1, 2003; United States. (digital.library.unt.edu/ark:/67531/metadc933932/: accessed October 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.