Both real-time rate and resolution both are key indexes of Synthetic Aperture Radar(SAR)imaging, but there is a
conflict between them. Real-time imaging becomes difficult because of the large computational requirement posed by
high-resolution processing. Parallel computing is an effective approach for real-time processing. In previous research,
coarse and medium grained parallel algorithms for SAR imaging have been presented. Although they can significantly
improve the processing speed, the quality of image has been ignored. Subaperture is widely used in high-resolution SAR.
Compared with full aperture processing, it can compensate the motion errors more accurately and get better images.
Whereas, subaperture processing can't be applied in existing parallel imaging algorithms because of they are all based
on full aperture processing, which restricts the application of existing algorithms in high-resolution SAR parallel
imaging. This paper presents a parallel imaging algorithm for
high-resolution SAR, through which we can obtain
high-resolution SAR image while achieving good computation efficiency. It combines chirp-scaling algorithm with
subaperture processing. The new algorithm can highly effectively run on parallel computer, in which each node has the
same load. It reduces the large communication requirement posed by three transposes through designing CS processing
for subaperture data, and it has better parallel scalability, which means that it can be used on larger parallel computer
without deducing the image quality. The experiments on SGI Origin2000 have proved that, compared with medium
grained parallel CS algorithm, the algorithm presented in this paper is more suitable for high-resolution SAR parallel