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With the large amount of image data that can be produced in real-time by new synthetic aperture radar (SAR) platforms, such as Global Hawk, compression techniques will be needed for both transmission and storage of this data. Also to keep image analysts (IA's) from being overwhelmed, high-speed automatic target cueing and/or recognition (ATC, ATR) systems will be needed to help exploit this large amount of data in real-time. Past SAR image compression studies have used subjective visual ratings and/or statistical measures such as mean-squared-error (MSE) to compare compression performance. Statistical metrics are much more appealing than unreproducible biased visual interpretations. However, the use of statistical metrics, such as MSE, has practical limitations on SAR imagery due to the high frequency speckle noise that is characteristic. In this case, the MSE metric is dominated by how well the noise speckle is preserved -- a statistic that is of no consequence. Since the large amount of data that dictates the need for compression also dictates the need for ATR, a meaningful statistic would be ATR performance. This ATR performance metric would emphasize how well pixels on target are preserved. Therefore, we have investigated ATR performance using a wavelet compression technique, since this technique has achieved very high compression on other types of imagery. We have used the Rice University Computational Mathematics Laboratory's wavelet compression algorithm in conjunction with a 'synthetic discriminant function' (SDF) based ATR algorithm. The SDF technique was developed at Carnegie Mellon University and successfully applied to SAR imagery by the Northrop Grumman Science & Technology Center. This combination allows ATR performance to be parameterized as a function of compression rate. The SAR data used for this research was taken from the public-released MSTAR target and clutter data set. We show results for both target detection and target identification versus false alarms for varying compression rates.
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