Paper
31 May 1996 Analysis of advantages and disadvantages of scene registration as part of space-time processing
Cynthia C. Piotrowski, Morton S. Farber, Stuart J. Hemple, William S. Helliwell
Author Affiliations +
Abstract
It is generally accepted that scene registration is an essential step in space-time detection processing. This paper isolates the effect of registration in order to evaluate its utility apart from its implications for filter construction or other implementation issues. Model calculations and simulation studies indicate that perfect registration can theoretically provide large gains in detection performance (up to approximately 9 dB) depending on clutter content and motion, but that the gains are quickly reduced in the presence of registration error. Registration error degrades performance in two ways: (1) it lowers the gain achievable by stacking, and (2) it results in increased clutter leakage. Registration of real data can never be perfect, of course, due to properties of physical edges in real data that the typical registration algorithm simply cannot handle, such as differential motion of background and foreground regions at a scene edge, and hiding and revealing of scene elements. Processing of several sequences of AIRMS data, with and without registration, using a three-dimensional (space-time) matched filter defined in the spectral domain, has shown that the overall gains for real data from a realizable registration algorithm are modest, usually less than 3 dB, for a sensor with relatively little jitter.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cynthia C. Piotrowski, Morton S. Farber, Stuart J. Hemple, and William S. Helliwell "Analysis of advantages and disadvantages of scene registration as part of space-time processing", Proc. SPIE 2759, Signal and Data Processing of Small Targets 1996, (31 May 1996); https://doi.org/10.1117/12.241160
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KEYWORDS
Sensors

Clouds

Image registration

Signal to noise ratio

Data modeling

Target detection

Performance modeling

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