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Proceedings Paper

Preliminary results of SAR image compression using MatrixView on coherent change detection (CCD) analysis
Author(s): Lawrence S, Gresko; LeRoy A. Gorham; Arvind Thiagarajan
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Paper Abstract

An investigation was made into the feasibility of compressing complex Synthetic Aperture Radar (SAR) images using MatrixViewTM compression technology to achieve higher compression ratios than previously achieved. Complex SAR images contain both amplitude and phase information that are severely degraded with traditional compression techniques. This phase and amplitude information allows interferometric analysis to detect minute changes between pairs of SAR images, but is highly sensitive to any degradation in image quality. This sensitivity provides a measure to compare capabilities of different compression technologies. The interferometric process of Coherent Change Detection (CCD) is acutely sensitive to any quality loss and, therefore, is a good measure by which to compare compression capabilities of different technologies. The best compression that could be achieved by block adaptive quantization (a classical compression approach) applied to a set of I and Q phased-history samples, was a Compression Ratio (CR) of 2x. Work by Novak and Frost [3] increased this CR to 3-4x using a more complex wavelet-based Set Partitioning In Hierarchical Trees (SPIHT) algorithm (similar in its core to JPEG 2000). In each evaluation as the CR increased, degradation occurred in the reconstituted image measured by the CCD image coherence. The maximum compression was determined at the point the CCD image coherence remained > 0.9. The same investigation approach using equivalent sample data sets was performed using an emerging technology and product called MatrixViewTM. This paper documents preliminary results of MatrixView's compression of an equivalent data set to demonstrate a CR of 10-12x with an equivalent CCD coherence level of >0.9: a 300-400% improvement over SPIHT.

Paper Details

Date Published: 2 May 2012
PDF: 6 pages
Proc. SPIE 8394, Algorithms for Synthetic Aperture Radar Imagery XIX, 839405 (2 May 2012); doi: 10.1117/12.925068
Show Author Affiliations
Lawrence S, Gresko, MV Holding Corp. (United States)
LeRoy A. Gorham, Air Force Research Lab. (United States)
Arvind Thiagarajan, MV Holding Corp. (United States)


Published in SPIE Proceedings Vol. 8394:
Algorithms for Synthetic Aperture Radar Imagery XIX
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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