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SAR data processing has matured over the pst decade with development in processing approaches that include traditional time-domain methods, popular and efficient frequency-domain methods, and relatively new and more precise chirp-scaling methods. These approaches have been used in various processing applications to achieve various degrees of efficiency and accuracy. One common trait amongst all SAR data processing algorithms, however, is their iterative and repetitive nature that make them amenable to parallel computing implementation. With SAR's contribution to remote sensing now well-established, the processing throughout demand has steadily increased with each new mission. Parallel computing implementation of SAR processing algorithms is therefore an important means of attaining high SAR data processing throughput to keep up with the ever- increasing science demand. This paper concerns parallel computing implementation of a mode of data called ScanSAR. ScanSAR has the unique advantage of yielding wide swath coverage in a single data collection pass. This mode of data collection has been demonstrated on SIR-C and is being used operationally for the first time on Radarsat. The burst nature of ScanSAR data is a natural candidate for parallel computing implementation. This paper gives a description of such an implementation experience at Alaska SAR Facility for Radarsat ScanSAR mode data. A practical concurrent processing technique is also described that allows further improvement in throughput at a slight increase in system cost.
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