面向弹载SAR系统精确制导应用的ROI智能快速检测

张军; 高贵; 黄英君; 杨海滨

doi:10.3724/SP.J.1146.2008.00490

面向弹载SAR系统精确制导应用的ROI智能快速检测

doi: 10.3724/SP.J.1146.2008.00490

基金项目:

国家自然科学基金(60772045)资助课题

计量
- 文章访问数: 3259
- HTML全文浏览量: 87
- PDF下载量: 846
- 被引次数: 0
出版历程
- 收稿日期: 2008-04-24
- 修回日期: 2008-11-11
- 刊出日期: 2009-06-19

Fast and Intelligent Detection of ROI in Precision-Guided Applications of Missile-Borne SAR System

摘要

摘要: 该文针对导弹精确打击的任务需求，以基于人在回路合成孔径雷达(Synthetic Aperture Radar，SAR)图像压缩传输系统的实现为主要目标，根据导弹精确制导所感兴趣目标的非特定性以及强散射性等共同特点，提出了一种基于任务驱动的SAR图像目标智能快速检测统一框架。该框架从检测的实用性对检测算法的智能、快速、高性能、可辨识以及对图像分解尺度的鲁棒性等因素出发，以两步CFAR(Constant False-Alarm Rate)检测为核心步骤实现目标的检测，即用结构分量上的快速预览来指导原始图像上的智能快速检测。理论和实验都证明文中所提框架是一种针对不同目标总体流程相同，局部实现细节相异的检测方案，具有一定的实用性和良好的可扩充性。
- 合成孔径雷达;人在回路;压缩传输;感兴趣目标
Abstract: For the needs of precision-guided striking task and to develop an image compression and translation system which based on man-in-loop SAR, a general task-drive intelligent and fast detection framework of SAR images is proposed according to the same character of non-specific and strong scattering in ROI of precision guided missiles. Started from practicality of the detection which need the algorithm be intelligent, fast, high performance, this framework takes two-step CFAR detection as the core step, which used the rapid structural component preview to guide the intelligent and rapid detection in original image. Theoretical analysis and experimental results show that this framework is a good detecting scheme which has same overall flow and different local implementation details for different goal, so it has strong practicality and excellent expandability.

HTML全文

参考文献(1)

常青. 巡航导弹制导系统关键技术研究[D]. [博士论文], 西北工业大学, 2003.Chang Q. Research of guidance techniques on Cruisemissile[D]. [Ph.D. dissertation], Northwestern PolytechnicUniversity, 2003.[2]Nahm J W and Smith M J. Optimized encoder designalgorithm for joint compression and recognition[J]. Proc.SPIE, Signal Processing, Sensor Fusion, and TargetRecognition IV, 07/1995, Vol. 2484: 236-245.[3]Nasser M N, Joseph P S, Heesung K, and Syed A R. Highcompression of SAR imagery for battlefield surveillance[J].Proc. SPIE, Algorithms for Synthetic Aperture RadarImagery III, 06/1996, Vol. 2757: 397-408.[4]宋荣华, 宋建社, 薛文通, 袁礼海. SAR 图像压缩技术的发展与现状[J]. 计算机应用研究, 2005, 22(4): 6-8.Song Y H, Song J S, Xue W T, and Yuan L H. Developmentand actuality of SAR image compression techniques[J].Application Research of Computers, 2005, 22(4): 6-8.[5]高贵. SAR 图像目标ROI 自动获取技术研究[D]. [博士论文],国防科学技术大学, 2007.Gao G. The research on automatic acquirement of targetsROI from imagery[D]. [Ph.D. dissertation], NationalUniversity of Defense Technology, 2007.[6]曾国奇, 苏东林, 刘焱. 雷达目标特征数据库在雷达组网仿真上的应用[J].电子与信息学报.2007, 29(7):1683-1686浏览[7]Oliver C J and Quegan S. Understanding Synthetic ApertureRadar Images[M]. Boston London: Artech House: Boston,1998: 88-204.[8]Novak L M, et al.. The automatic target-recognition systemin SAIP[J]. The Lincoln Laboratory Journal, 1997, 10(2):187-202.[9]English R A, et al.. Development of an ATR workbench forSAR imagery[R]. Technical Report, DRDC, Ottawa, 2005.[10]McConnell I and Oliver C J. A comparison of segmentationmethods with standard CFAR for point target detection[C].Europto Conf. on SAR Image Analysis, Simulation andModelling IV, SPIE Proc., 1998, 3497: 76-87.[11]Yin W, Goldfarb D, and Osher S. Total variation based imagecartoon-texture decomposition[R]. Technical Report CORCTR-2005-01,Columbia University, USA, 2005.[12]张军, 高贵. SAR 图像目标检测的两种CFAR 算法对比研究[J]. 信号处理, 2008, 24(1): 78-82.Zhang J and Gao G. Comparison on two CFAR algorithms ofvehicle target detection in SAR Images[J]. Signal Processing,2008, 24(1): 78-82.[13]Frery A C, et al.. A model for extremely heterogeneousclutter[J]. IEEE Trans. on GRS, 1997, 35(3): 648-659.[14]邹焕新. SAR 图像舰船目标与航迹检测方法研究[D]. [博士论文], 国防科技大学, 2003.Zou H X. Research on detection of ship and ship wakes fromSAR image[D]. [Ph.D. dissertation], National University ofDefense Technology, 2003.

施引文献

资源附件(0)

访问统计