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Proceedings Paper

Adaptive compression algorithm results for complex synthetic aperture radar data
Author(s): Francis R. Cirillo; Paul L. Poehler; Noneen Ziemba
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Paper Abstract

Research conducted on complex Synthetic Aperture Radar (SAR) data over the last two years has culminated in the development of a compression algorithm1 compatible with current imagery standards. This new algorithm also includes adaptive attributes which identify the radar data type, data characteristics, and then selects optimal quantization parameters, generated based on the statistics of the data, from a knowledge base. This algorithm has achieved near-lossless compression ratios in excess of 20 to 1, with reduced Root Mean Square Error (RMSE) and increased Peak Signal to Noise Ratio (PSNR). This algorithm also produces minimal degradation when producing phase-derived radar products. This paper describes the algorithm development, operation, and test results obtained using this compression algorithm., The algorithm component elements are described including the use of an adaptive preprocessor, modified quantizer, and knowledge base. This paper details the improved results observed for compressed data, magnitude imagery, and phase-derived products generated during the study.

Paper Details

Date Published: 12 September 2003
PDF: 7 pages
Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); doi: 10.1117/12.514650
Show Author Affiliations
Francis R. Cirillo, Science Applications International Corp. (United States)
Paul L. Poehler, Science Applications International Corp. (United States)
Noneen Ziemba, Science Applications International Corp. (United States)

Published in SPIE Proceedings Vol. 5095:
Algorithms for Synthetic Aperture Radar Imagery X
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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