The geometric correction of SAR imagery is an important step in SAR image processing. This process has a certain degree of computational complexity and requires a certain geometric positioning model. A GPU-supported massively parallel processing method is presented for the geometric correction of space-borne SAR imagery. This method exploits the RPC model. The method takes full advantage of two facts. A GPU has large computational resources

and the processing steps are the same for each pixel in geometric correction. In the course of massively parallel processing

a large amount of pixels are imported into the GPU at each time

and one thread is allocated for one pixel. Each thread performs the steps

which include the calculation of rational function

transformation for projection

resampling

and so on

with high computational complexity. The optimal configuration of two parameters

i.e.

dimGrid and dimBlock

improves parallel performance. Large SAR image frames with different sizes can be processed by block partition. Experimental results show that the proposed method can achieve computational speedups ranging from 38 to 44. Meanwhile

the speedup for the whole procedure is recorded to analyze the features of the GPU-based parallel computing objectively and thoroughly. The factors affecting the speedup for the whole procedure are discussed according to the results of several experiments

and an optimal approach that reads and writes a large block to promote I/O performance is proposed. The straightforward method has broad applicability and can be used for most space-borne SAR sensors and for different image frame sizes.The method also achieves obvious acceleration.