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基于假设检验及SAR图像统计分布特性的伪装效果评价方法 被引量:1

A camouflage effectiveness assessing method based on hypothesis testing and the characteristic of SAR image
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摘要 高分辨率SAR雷达在军事领域的广泛应用,使得针对SAR侦察的军事目标伪装效果评价需求日益迫切。利用试验获取的SAR图像,分析了伪装目标和背景的高分辨率SAR图像的分布特性,引入假设检验理论,验证了统计分布特性,构建了伪装效果评价算法。根据试验得到的高分辨率SAR图像的判读结果与算法实验结果比对,验证了方法的有效性。 With the high resolution SAR applied widely in the military field,camouflage effectiveness assessment for military targets against SAR is urgently needed.Using SAR images acquisited from the test,distribution characteristics of the camouflaged targets and background in high resolution SAR images were analysed.The theory of hypothesis testing was introduced,the statistical distribution characteristics were verified.A new camouflage effectiveness assessing method was given.The contrast results of the high resolution SAR image interpretation and results experimental of the algorithm prove the validity of the method.
出处 《吉林大学学报(工学版)》 EI CAS CSCD 北大核心 2013年第S1期313-316,共4页 Journal of Jilin University:Engineering and Technology Edition
关键词 伪装效果评价 SAR图像 假设检验 统计分布特性 camouflage effectiveness assessment SAR image hypothesis testing statistical distribution characteristics
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  • 1L. Xu,P. Yan,T. Chang. Best First Strategy for Feature Selection[ A ]. Proc. Ninth Int' l Conf. Pattern Recognition [ C]. 1988. 706-708.
  • 2J. Yang,V. Honavar. Feature Subset Selection Using A Genetic Algorithm [ J ]. Feature Extraction, Construction and Selection :A Data Mining Perspective. 1998.117-136.
  • 3L. Yu, H. Liu. Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution [ A ]. Proc. 20th Int' l Conf. Machine Learning [ C]. 2003. 856-863.
  • 4王岩.机载SAR目标特征提取与识别方法研究[M].博士学位论文.长沙:国防科学技术大学研究生院.2003.9.
  • 5Zhaohui Chen. Machine Learning Approach Towards Automatic Target Recognition. A thesis for the doctor' s degree. Cambridge, Massachusetts, USA : Harvard University. August,2001.
  • 6Anil K. Jain, Robert P. W. Duin, and Jianchang Mao. Statistical Pattern Recognition : A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 4-37, January 2000.
  • 7L. M. Novak, et al. The Automatic Target Recognition System in SAIP. The Lincoln Laboratory Journal, 1997, 10 (2) :187 -202.
  • 8M. Greenspan,et al. Development and Evaluation of a Real Time SAR ATR System. IEEE, 1998:38- 43.
  • 9R. A. English, et al. DeveLopment of an ATR Workbench for SAR Imagery. Technical Report, DRDC ,Ottawa,2005.
  • 10J. C. Oliver, et al. http ://www. infosar, co. uk/misc/demo. html.

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