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基于GLRT的红外多光谱弱小运动目标检测 被引量:2

Dim moving target detection in multispectral IR image sequence based on GLRT
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摘要 针对多光谱红外图像序列中未知光谱辐射强度、位置和速度的弱小运动目标检测问题,建立了与之相应的基本框架模型.对于这种含参信号的复合假设检验问题,采用广义似然比检验(GLRT)得到了该问题的检测算子,同时利用速度滤波器组在实际应用中实现了该检测算子.从理论角度估计了该算法的虚警概率和检测概率,并通过计算机仿真验证了上述分析结果.为了评估该算法的有效性,采用人工合成的多光谱红外图像序列对其进行测试,结果说明该算法对于低信噪比条件下的弱小运动目标具有良好的检测效果. The scope of this paper addressed the problem of detecting a dim moving target from a sequence of multispectral IR cubes.The detection problem was formulated in a general framework,assuming unknown target amplitude,position and velocity.This composite hypothesis testing problem was approached by means of the generalized likelihood ratio test(GLRT) theory.The detector structure and its actual implementation based on velocity filters were discussed in detail.Approximated expressions of the false alarm and detection probabilities were obtained and validated by means of simulation.To test the effectiveness of the detection algorithm,the detection results obtained on a set of synthetic multispectral IR image sequences were presented and discussed.These results indicate that the algorithm proposed can obtain a good performance on dim target detection with low SNR.
出处 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2011年第2期149-155,共7页 Journal of Infrared and Millimeter Waves
基金 国家自然科学基金资助项目(60877065/F050401) 哈尔滨市科技局学科带头人基金(RC2008XK009004)
关键词 弱小目标检测 多光谱红外图像序列 广义似然比检验 速度滤波器 dim target detection multispectral IR image sequence generalized likelihood ratio test velocity filter
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  • 1Theodore A P. Explanation of why the sensor in the exoatmospheric kill vehicle (EKV) cannot reliably discriminate decoys from warheads [ R/OL]. http://fas, org/spp/starwars/program/news00/postol_atta, pdf.
  • 2Theodore A P. Technical discussion of the misinterpreted results of the IFT-1A experiment due to tampering with the data and analysis and errors in the interpretation of the data [ R/OL ]. http://fas, org/spp/starwars/progranr/newsO0/ postol attb. pdf.
  • 3操乐林,武春风,侯晴宇,张伟.基于光谱成像的目标识别技术综述[J].光学技术,2010,36(1):145-150. 被引量:17
  • 4黄士科,张天序,李丽娟,陈宝国.空空导弹多光谱红外成像制导技术研究[J].红外与激光工程,2006,35(1):16-20. 被引量:32
  • 5Stein D W J, Beaven S G, Hoff L E, et al. Anomaly detection from hyperspectral imagery [ J ]. IEEE Signal Processing Magazine,2002,19( 1 ) :58-69.
  • 6Yu X L, Hoff L E, Reed I S, et al. Automatic target detection and recognition in muhiband imagery: a unified ML detection and estimation approach[ J]. IEEE Trans. on Image Processing, 1997,6( 1 ) : 143-156.
  • 7Rallier G, Descombes X, Falzon F, et al. Texture feature analysis using a Gauss-Markov model in hyperspectral image classification[J]. IEEE Trans. on Geoscience and Remote Sensing, 2004,42 ( 7 ) : 1543-1551.
  • 8Chang C I. H?perspectral imaging: techniques for spectral detection and classification [ M ]. Kluwer Academic/Plenum Publishers, 2003,13-102.
  • 9Li N, Du P, Zhao H J. Independent component analysis based on improved quantum genetic algorithm: application in hyperspectral images [ J ]. International Geoscierwe and Remote Sensing Syml)osium ( IGARSS ) , 2005, 6 : 4323- 4326.
  • 10寻丽娜,方勇华,李新.高光谱图像中基于端元提取的小目标检测算法[J].光学学报,2007,27(7):1178-1182. 被引量:27

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