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Proceedings of SPIE: Algorithms for Synthetic Aperture Radar Imagery V, April 1998. (1998)
| Venue: | Proc. of SPIE: Algorithms for Synthetic Aperture Radar Imagery V |
BibTeX
@INPROCEEDINGS{Information98proceedingsof,
author = {Further Author Information and Frank M. C and Jose C. Principe},
title = {Proceedings of SPIE: Algorithms for Synthetic Aperture Radar Imagery V, April 1998.},
booktitle = {Proc. of SPIE: Algorithms for Synthetic Aperture Radar Imagery V},
year = {1998},
pages = {197--207}
}
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Abstract
This paper introduces a methodology for the superresolution of synthetic aperture radar (SAR) images using multiple target and clutter models. The system has two major components: a mechanism that selects the appropriate model for superresolution and a bank of model estimators to accomplish the superresolution. The typical point scatterer model is incorporated into this technique as well as a model for clutter. Other models can be naturally incorporated. This methodology is flexible in that it can utilize many of the well-known modern spectral estimation techniques. The ability to more accurately model targets using models other than the point scatterer as well as the importance of including models for clutter into a superresolution paradigm is addressed. These issues are shown to be relevant to the automatic target recognition / detection (ATR/D) problem. We present a comparison of our technique with other SAR imaging methods and discuss the relative benefits afforded by such an approach.
Keyphrases
synthetic aperture radar imagery major component model estimator appropriate model superresolution paradigm multiple target well-known modern spectral estimation technique point scatterer model target synthetic aperture radar clutter model automatic target recognition detection typical point scatterer model relative benefit
