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.
Existing compression algorithms, primarily designed for visible electro-optical (EO) imagery, do not work well for Synthetic Aperture Radar (SAR) data. The best compression ratios achieved to date are less than 10:1 with minimal degradation to the phase data. Previously, phase data has been discarded with only magnitude data saved for analysis. Now that the importance of phase has been recognized for Interferometric Synthetic Aperture Radar (IFSAR), Coherent Change Detection (CCD), and polarimetry, requirements exist to preserve, transmit, and archive the both components. Bandwidth and storage limitations on existing and future platforms make compression of this data a top priority. This paper presents results obtained using a new compression algorithm designed specifically to compress SAR imagery, while preserving both magnitude and phase information at compression ratios of 20:1 and better.
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