Ship detection is important in military and civilian applications. Synthetic Aperture Radar（SAR） with all-day

all-weather

and ultra-long-range characteristics has been extensively used. The two-parameter Constant False Alarm Rate（CFAR） method is one of the most well-known methods for target detection. CFAR is an adaptive threshold detection scheme that works efficiently when the background clutter is unevenly distributed. However

in recent years

the resolution of SAR images is significantly improved by the rapid development of the SAR sensor. With the improvement of the resolution

the size of SAR images significantly increased and the ship targets no longer appear as point targets. Instead

the ship targets appear as hard targets. The contour of the targets becomes clearer as well. When the two-parameter CFAR is used to detect ship targets with good contour

a longer computation time is needed. Message Passing Interface（MPI） parallelization is a workable solution used to shorten the computation time of two-parameter CFAR with MPI parallel technique.The traditional MPI parallelization divides the SAR image horizontally/vertically on average. However

in practical applications

preprocessing methods

such as land masking and geometric correction

are conducted before detection. These preprocessing methods can cause the uneven distribution of the points to be detected. This uneven distribution leads to the unbalanced tasks between the parallel processes.Thus

the efficiency of MPI parallelization is highly influenced. The objective of this study is to eliminate the negative influence caused by the uneven distribution.In this study

we propose an improved MPI parallel solution of the two-parameter CFAR ship detection method

in which the SAR image is divided in terms of the number of points to be detected. The partitioning strategy includes: First

the total number of points to be detected is calculated. Second

the approximate number of responsible points for each process is computed. Third

the responsible rows of image for each process are identified.In this manner

the entire detection task is equally divided among the processes. The details of the improved parallel algorithm are listed as below:（1） The first process computes the partitioning strategy and transmits it to the other processes.（2）Each process imports its responsible part of the image.（3）Each process implements two-parameter CFAR detection on its responsible part of the image.（4）The first process obtains the detection results from the other processes.The numerical experiment is conducted on a cluster computer. When the number of processes is 8

the speedup of the improved parallel algorithm is 7.45

which is better than that of the normal parallel algorithm. The efficiency of parallelization increases by approximately43%. A similar experiment is conducted on a multicore computer

and a similar result is obtained.The experimental results show that the improved parallel solution can shorten the detection time and improve the parallel efficiency of the cluster or multicore computer. This study is positively significant for real-time ship detection based on airborne SAR images. Further research is needed to shorten the detection time by using the GPU or Intel MIC architecture.