Task distribution balancing for parallel two-parameter CFAR ship detection

doi:10.11834/jrs.20165006

CORC > 自然资源部第一海洋研究所 > 自然资源部第一海洋研究所 > 业务部门 > 海洋物理与遥感研究室

	Task distribution balancing for parallel two-parameter CFAR ship detection
	Zhang, Linjie 1; Zhang, Jie 2; Zhang, Xi 2; Lang, Haitao 3
刊名	Yaogan Xuebao/Journal of Remote Sensing
	2016
卷号	20 期号:2 页码:344-351
关键词	Cluster computing Efficiency Errors Image enhancement Image resolution Message passing Military applications Parallel algorithms Parameter estimation Ships Synthetic aperture radar
ISSN号	10074619
DOI	10.11834/jrs.20165006
英文摘要	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 approximately 43%. 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. © 2016, Science Press. All right reserved.
电子版国际标准刊号	20959494
语种	中文
出版者	Science Press
内容类型	期刊论文
源URL	[http://ir.fio.com.cn/handle/2SI8HI0U/6598]
专题	业务部门_海洋物理与遥感研究室
作者单位	1.College of Mathematical Science, Ocean University of China, Qingdao; 266100, China;; 2.The First Institute of Oceanography, State Oceanic Administration, Qingdao; 266061, China;; 3.Physics and Electronics Department, Beijing University of Chemical Technology, Beijing; 100029, China
推荐引用方式 GB/T 7714	Zhang, Linjie,Zhang, Jie,Zhang, Xi,et al. Task distribution balancing for parallel two-parameter CFAR ship detection[J]. Yaogan Xuebao/Journal of Remote Sensing,2016,20(2):344-351.
APA	Zhang, Linjie,Zhang, Jie,Zhang, Xi,&Lang, Haitao.(2016).Task distribution balancing for parallel two-parameter CFAR ship detection.Yaogan Xuebao/Journal of Remote Sensing,20(2),344-351.
MLA	Zhang, Linjie,et al."Task distribution balancing for parallel two-parameter CFAR ship detection".Yaogan Xuebao/Journal of Remote Sensing 20.2(2016):344-351.