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Proceedings Paper

Predicting the effectiveness of SAR imagery for target detection
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Paper Abstract

We present an image quality metric and prediction model for SAR imagery that addresses automated information extraction and exploitation by imagery analysts. This effort drarws on our team's direct experience with the development of the Radar National Imagery Interpretability Ratings Scale (Radar NIIRS), the General Image Quality Equations (GIQE) for other modalities, and extensive expertise in ATR characterization and performance modeling. In this study, we produced two separate GIQEs: one to predict Radar NIIRS and one to predict Automated Target Detection (ATD) performance. The Radar NIIRS GIQE is most significantly influenced by resolution, depression angle, and depression angle squared. The inclusion of several image metrics was shown to improve performance. Our development of an ATD GIQE showed that resolution and clutter characteristics (e.g., clear, forested, urban) are the dominant explanatory variables. As was the case with NIIRS GIQE, inclusion of image metrics again increased performance, but the improvement was significantly more pronounced. Analysis also showed that a strong relationship exists between ATD and Radar NIIRS, as indicated by a correlation coefficient of 0.69; however, this correlation is not strong enough that we would recommend a single GIQE be used for both ATD and NIIRS prediction.

Paper Details

Date Published: 4 May 2011
PDF: 12 pages
Proc. SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, 805110 (4 May 2011); doi: 10.1117/12.882712
Show Author Affiliations
Daniel Gutchess, Charles River Analytics, Inc. (United States)
John M. Irvine, Draper Lab. (United States)
Mon Young, Draper Lab. (United States)
Magnús S. Snorrason, Charles River Analytics, Inc. (United States)


Published in SPIE Proceedings Vol. 8051:
Algorithms for Synthetic Aperture Radar Imagery XVIII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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