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Proceedings Paper

Nonparametric data modeling in SAR image quality assessment
Author(s): Johnathan D. Michel; Qin Cai; Keith C. Drake
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Paper Abstract

With the growing size of target databases and the large number of example images required for target recognition system development, a key requirement in managing ATR system development is the automatic and accurate assessment of target imagery. We define assessment in terms of image similarity of the target subimage to a truth target image set. The goal in this work is to create a system that automates the assessment of images and improves the accuracy of the image database assessment process. Our approach to the database assessment problem combines an image feature based approach with a statistical data modeling approach. The process being two-fold, provides a generic framework for approaching the problem regardless of imaging modalities. The image assessment process must handle a range of both high-level tasks as well as low level tasks, e.g., identifying Regions of Interest, segmenting the target, and computing feature based image metrics and statistical distances between images. This work describes the design and work in progress on the implementation of such a system.

Paper Details

Date Published: 15 September 1998
PDF: 8 pages
Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); doi: 10.1117/12.321820
Show Author Affiliations
Johnathan D. Michel, AbTech Corp. (United States)
Qin Cai, AbTech Corp. (United States)
Keith C. Drake, AbTech Corp. (United States)


Published in SPIE Proceedings Vol. 3370:
Algorithms for Synthetic Aperture Radar Imagery V
Edmund G. Zelnio, Editor(s)

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