天空起伏背景中红外弱小目标检测新方法研究
|
摘要
本文围绕天空起伏背景中红外弱小目标的检测问题,展开以下研究：
1、在分析背景杂波与弱小目标的图像特性基础上,研究红外图像中弱小目标与背景杂波在空域和时域中的可分离性,对不同特征的可分离性进行分析和比较,确定了在空域强相关的背景杂波灰度分布特征、在连续帧间强相关的弱小目标灰度特征以及在连续帧间强相关的弱小目标位置特征作为本文算法研究的出发点；
2、在分析背景预测模型的基础上,比较固定权值和自适应权值的背景预测算法,依据背景杂波灰度分布在空域强相关的图像特征,提出一种局域同质背景预测算法,这种方法可以克服异质背景区域对预测精度的影响,大幅减少不必要的预测运算以及由此带来的运算误差,显著提高残差图中弱小目标信号的信杂比,改善背景预测方法检测弱小目标的性能；
3、提出一种对比滤波的时域廓线算法,针对传统方法当弱小目标与云边缘杂波的运动速度相当时出现虚警的情况,新方法在分析弱小目标、云边缘杂波以及平稳背景三类像素点时域特性的基础之上,首先利用时域廓线特性抑制平稳背景,然后依据云边缘杂波像素在空域连续,而弱小目标像素在空域孤立的特性,构造空域对比滤波器,并使用对比滤波器对去除平稳背景的图像数据进行滤波,最后再进行时域廓线的驻点连线滤波以实现对弱小目标的检测,该算法能明显消除与弱小目标运动速度相当的云边缘杂波虚警,进一步提高弱小目标的检测概率；
4、提出一种运动方向估计的管道滤波算法,分析了红外弱小目标的运动特性,依据弱小目标在相邻帧间位置具有连贯性的特征,建立了弱小目标的运动方向估计模型,在模型中利用弱小目标逐帧检测的先验位置信息,估计弱小目标的运动方向和轨迹,根据估计结果去除管道内噪声对弱小目标的干扰。仿真结果表明,本方法能够很好地抑制管道内噪声的影响,提高弱小目标的检测概率,增强弱小目标抗管道内噪声干扰的能力。
The following studies are carried out on detection of infrared dim small target in cluttered sky background in this paper.
The separable features of dim small target and background in temporal domain and spacial domain were studied based on the analysis of image characteristic of background and targets. The separable characteristics were analyzed and compared in performance. Taking feature of gray distribution of fluctuant background strongly correlated in space, feature of target gray strongly correlated between consecutive frames and feature of target location strongly correlated between consecutive frames as the separable characteristics adopted by algorithms in this paper.
Based on the analysis of the model of background prediction, the definite weight model and the adaptive model were compared in perfromance. Taking image feature of gray distribution of fluctuant background in strong correlation space as the reliable space separable characteristic between background and dim small targets, a local homogeneous background prediction methods was presented to overcome the influence on the prediction accuracy from heterogeneous background region. As a result, the unnecessary prediction operations and derived operation errors were sharply decreased, signal clutter ratio in the residual image was largely increased, and the detection performance of dim small target was improved.
A temporal profile algorithm was proposes based on comparison filtering as a responding method to the fake-alarm occurrence existing in the traditional detection algorithm when the dim small target has equivalent velocity with that of cloud edge clutters. Based on the analysis on the time domain characteristics of the dim small target, cloud edge clutters as well as the stationary background, the characteristic of the temporal profile is adopted to restrain the stationary background, then the spatial domain comparison filter is structured based on the fact that the pixels of the cloud edge clutters are continuous in spatial domain while the pixels of dim small target are discrete, and the images after removal of the static background are filtered with comparison filter; lastly, connecting line of the stagnation points based filtering is used to realize the detection of dim small target. Simulation data show that this algorithm can significantly eliminate the fake-alarm caused by the cloud edge clutters with equivalent velocity of the target, thus further improve the detection probability of dim small target.
A pipeline filter algorithm based on motion direction estimation was suggested as a method to improve the flaw of detection probability deduction due to the strong interferential noise within the pipeline and low signal noise ratio. The method analyzed the motion characteristics of infrared dim small target and establishes the motion direction estimation model according to the continuity characteristic of the targets between consecutive frames. Through the model, the prior position information of the targets is detected frame by frame and analyzed in order to estimate the motion direction and trajectory of the targets. The estimation results are made use to eliminate the interference on the targets caused by the pipeline inner noises. Experiments and simulation results show that the algorithm can suppress noises in the pipeline effectively, increase the detection probability of the targets, and strengthen the resistance characteristic of the targets against the noises within the pipe.
1、在分析背景杂波与弱小目标的图像特性基础上,研究红外图像中弱小目标与背景杂波在空域和时域中的可分离性,对不同特征的可分离性进行分析和比较,确定了在空域强相关的背景杂波灰度分布特征、在连续帧间强相关的弱小目标灰度特征以及在连续帧间强相关的弱小目标位置特征作为本文算法研究的出发点；
2、在分析背景预测模型的基础上,比较固定权值和自适应权值的背景预测算法,依据背景杂波灰度分布在空域强相关的图像特征,提出一种局域同质背景预测算法,这种方法可以克服异质背景区域对预测精度的影响,大幅减少不必要的预测运算以及由此带来的运算误差,显著提高残差图中弱小目标信号的信杂比,改善背景预测方法检测弱小目标的性能；
3、提出一种对比滤波的时域廓线算法,针对传统方法当弱小目标与云边缘杂波的运动速度相当时出现虚警的情况,新方法在分析弱小目标、云边缘杂波以及平稳背景三类像素点时域特性的基础之上,首先利用时域廓线特性抑制平稳背景,然后依据云边缘杂波像素在空域连续,而弱小目标像素在空域孤立的特性,构造空域对比滤波器,并使用对比滤波器对去除平稳背景的图像数据进行滤波,最后再进行时域廓线的驻点连线滤波以实现对弱小目标的检测,该算法能明显消除与弱小目标运动速度相当的云边缘杂波虚警,进一步提高弱小目标的检测概率；
4、提出一种运动方向估计的管道滤波算法,分析了红外弱小目标的运动特性,依据弱小目标在相邻帧间位置具有连贯性的特征,建立了弱小目标的运动方向估计模型,在模型中利用弱小目标逐帧检测的先验位置信息,估计弱小目标的运动方向和轨迹,根据估计结果去除管道内噪声对弱小目标的干扰。仿真结果表明,本方法能够很好地抑制管道内噪声的影响,提高弱小目标的检测概率,增强弱小目标抗管道内噪声干扰的能力。
The following studies are carried out on detection of infrared dim small target in cluttered sky background in this paper.
The separable features of dim small target and background in temporal domain and spacial domain were studied based on the analysis of image characteristic of background and targets. The separable characteristics were analyzed and compared in performance. Taking feature of gray distribution of fluctuant background strongly correlated in space, feature of target gray strongly correlated between consecutive frames and feature of target location strongly correlated between consecutive frames as the separable characteristics adopted by algorithms in this paper.
Based on the analysis of the model of background prediction, the definite weight model and the adaptive model were compared in perfromance. Taking image feature of gray distribution of fluctuant background in strong correlation space as the reliable space separable characteristic between background and dim small targets, a local homogeneous background prediction methods was presented to overcome the influence on the prediction accuracy from heterogeneous background region. As a result, the unnecessary prediction operations and derived operation errors were sharply decreased, signal clutter ratio in the residual image was largely increased, and the detection performance of dim small target was improved.
A temporal profile algorithm was proposes based on comparison filtering as a responding method to the fake-alarm occurrence existing in the traditional detection algorithm when the dim small target has equivalent velocity with that of cloud edge clutters. Based on the analysis on the time domain characteristics of the dim small target, cloud edge clutters as well as the stationary background, the characteristic of the temporal profile is adopted to restrain the stationary background, then the spatial domain comparison filter is structured based on the fact that the pixels of the cloud edge clutters are continuous in spatial domain while the pixels of dim small target are discrete, and the images after removal of the static background are filtered with comparison filter; lastly, connecting line of the stagnation points based filtering is used to realize the detection of dim small target. Simulation data show that this algorithm can significantly eliminate the fake-alarm caused by the cloud edge clutters with equivalent velocity of the target, thus further improve the detection probability of dim small target.
A pipeline filter algorithm based on motion direction estimation was suggested as a method to improve the flaw of detection probability deduction due to the strong interferential noise within the pipeline and low signal noise ratio. The method analyzed the motion characteristics of infrared dim small target and establishes the motion direction estimation model according to the continuity characteristic of the targets between consecutive frames. Through the model, the prior position information of the targets is detected frame by frame and analyzed in order to estimate the motion direction and trajectory of the targets. The estimation results are made use to eliminate the interference on the targets caused by the pipeline inner noises. Experiments and simulation results show that the algorithm can suppress noises in the pipeline effectively, increase the detection probability of the targets, and strengthen the resistance characteristic of the targets against the noises within the pipe.
引文
[1]常本康,蔡毅,红外成像阵列与系统,科学出版社,2006
[2]M. Diani, N. Acito, G. Corsini. Airborne threat detection in navy IRST systems [J]. IEE Proc., Vis. Image Signal Process. Vol.152:pp.45-51,2005
[3]方斌,陈天如,空空导弹红外成像制导关键技术分析[J],红外技术,Vo1.25(4):pp.45-48,2003
[4]奚云,红外告警技术[J],红外,Vo1.23(5):pp.10-14,2002
[5]田裕鹏,红外检测与诊断技术,化学工业出版社,2006
[6]Osmer, S. R. Nash, L. Hover, G. L.; et, al. Preliminary Assessment of U.S. Coast Guard Short Range Recovery (SRR) Forward Looking Infrared (FLIR) System Small Target Detection Performance. Interim rept. Sep 81-May 82
[7]B.D. Billard, M. Arozullah, J.T. Barnett. Application of nonparametric detectors to point target detection in infrared clutter backgrounds [C]. Proc. SPIE, Vol.1096: pp.108-18,1989
[8]John F. Bronskill, John S.A. Hepburn, Wing K. Au. Knowledge-based approach to the detection, tracking and classification of target formations in infrared image sequences [C]. Proc. IEEE Comput Soc Conf Comput Vision Pattern Recognit, pp.153-158, 1989
[9]R. Guissin. Infrared target acquisition using an adaptive Difference-of-Boxes filter [C]. Proc. SPIE, Vol.1157:pp.59-68,1989
[10]曹原,杨杰,刘瑞明.基于邻域分析TDLMS滤波器的红外小目标检测[J].红外与毫米波学报,Vol.28(3):pp.235-240,2009
[11]Meng Li, Tianxu Zhang, Weidong Yang, et, al. Moving dim small target detection and estimation with three-dimensional double directional filter in IR cluttered background [J]. Optical Engineering, Vol.44(10):pp.107007-1-4,2005
[12]T. Soni, J.R. Zeidler, W.H. Ku. Recursive estimation techniques for detection of small objects in infrared image data [C]. IEEE Proceedings International Conference on Acoustics Speech and Signal Processing. Vol.3:pp.581-584,1992
[13]T. V. DeMars. Adaptive dim point target detection and tracking in infrared images. ADA2048452,1988
[14]Himayat N, Kassam S. A. A structure for adaptive order statistics filtering [J]. IEEE Transactions on Image Processing. Vol.3(3):pp.265-280,1994
[15]Kotropoulos C, Pitas I. Adaptive LMS L-filters for noise suppression in images [J]. IEEE Transactions on Image Processing. Vol.5(12):pp.1596-1609,1996
[16]Muller M. Saliency measures in cluttered IR images for ATR [J]. Proc. SPIE, Vol.3699:pp.150-154,1999
[17]Barnett J. Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds [C]. Proc. SPIE, Vol.1050:pp.10-18, 1989
[18]Nichols S A, Naylor R B. Reliable motion detection of small targets in video with low signal-to-clutter ratios [C]. Proceedings of IEEE Annual International Camahan Conference on Security Technology, Vol.2561:pp.447-456,1995
[19]Scharfand L L, Friedlander B. Matched subspace detectors [J]. IEEE Transactions on Signal Processing. Vol.42(8):pp.2146-2157,1994
[20]Tom V. T, Peli. T, Leung M. et al. Morphology-based algorithm for point target detection in infrared backgrounds [C]. Proc. SPIE, Vol.1954:pp.2-11,1993
[21]叶斌,彭嘉雄,卢汉清.基于顺序形态滤波的红外运动小目标检测[J].数据采集与处理,Vol.16(3):pp.315-319,2001
[22]叶斌,彭嘉雄.基于形态学Top-Hat算子的小目标检测方法[J].中国图像图形学报,Vol.9(7):pp.638-642.2002
[23]曾明,李建勋.基于自适应形态学Top-Hat滤波器的红外弱小目标检测方法[J].上海交通大学学报,Vol.40(1):pp.90-93,2006
[24]Jian-Nan Chi, Ping Fu, Dong-Shu Wang, et al. A detection method of infrared image small target based on order morphology transformation and image entropy difference [C]. IEEE Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vol.8:pp.5111-5116,2005
[25]Zaveri M A, Merchant S N, Desai U B. Arbitrary trajectories tracking using multiple model based particle filtering in infrared image sequence [C]. IEEE Proceedings. ITCC 2004. International Conference on Information Technology:Coding and Computing, Vol.1:pp.603-607,2004
[26]Matthews K E, Namazi N M. A Bayes Decision Test for Detecting Uncovered-Background and Moving Pixels in Image Sequences [J]. IEEE Transactions on Image Processing. Vol.7(5):pp.720-728,1998
[27]Tonissen S M, Bar-Shalom Y. Detection of Low SNR Moving Targets with Time Varying Amplitude [C]. Fourth International Symposium on Signal Processing and its Applications, Vol.1:pp.29-32,1996
[28]Silverman J, Mooney J M, Caefer C E. Temporal filters for tracking weak slow point targets in evolving cloud clutter [J]. Infrared Physics & Technology. Vol.37(6):pp.695-710,1996
[29]Robin N. S, He H. Wavelet transform methods for object detection and recovery [J]. IEEE Transactions on Image Processing. Vol.6(5):pp.724-735,1997
[30]李国宽,彭嘉雄.基于小波变换的红外成像弱小目标检测方法[J].华中理工大学学报,Vo1.28(5)：69-71,2000
[31]柳钢,翟林培,贾新宇等.采用小波能量方法的海空背景中多目标检测与跟踪[J].光电工程,Vo1.31(11)：16-19,2004
[32]牟松涛,苏锦鑫,吴建东.基于小波变换的红外图像弱小目标检测研究[J].红外与激光工程,Vo1.33(5)：488-492,2004
[33]孙玉秋,田金文.基于双重能量累积的红外序列图像目标检测[J].长江大学学报,Vo1.2(1)：61-64,2005
[34]许彬,郑链,王克勇等.基于信号奇异性分析的小目标检测方法[J].红外技术,Vo1.27(3)：245-249,2005
[35]许彬,郑链,王克勇等.基于局部纹理分析的小目标检测方法[J].光电工程,Vo1.32(1)：40-42,2005
[36]彭嘉雄,周文琳.红外背景抑制与小目标分割检测[J],电子学报,Vo1.27(12):pp.47-51,1999
[37]Honggang Wu, Xiaofeng Li, Yuebin Chen, et al. Nonparametric Complex Background Prediction Algorithm Using FCM Cluttering for Dim Point Infrared Targets Detection [C].2006 International Conference on Communications, Circuits and Systems, Vol.1:pp.222-225,2006
[38]Zhenxue Chen, Guoyou Wang, Jianguo Liu, et al. Small Target Detection Algorithm Based on Average Absolute Difference Maximum and Background forecast [J]. International Journal of Infrared and Millimeter Waves. Vol.28(1): pp.87-97,2007
[39]Xu Kaiyu, Hu Wenhua, Zhou Weina, et al. Target Detection Based on The Artificial Neural Network Technology [C].9th International Conference on Control, Automation, Robotics and Vision, Vol.1:pp.1-5,2006
[40]Burton M, Benning C. Comparison of imaging infrared detection algorithms [C]. Proc. SPIE, Vol.302:26-32,1981
[41]Narayanan K A, Rosenfeld A. Image Smoothing by Local Use of Global Information [J]. IEEE Transaction on Systems, Man and Cybernetics, Vol.SMC-11(12):pp.826-831,1981
[42]王广君,田金文,柳健.基于局部熵的红外图像小目标检测[J].红外与激光工程,Vol.29(4):pp.26-29,2000
[43]Soni T, Zeidler J. Ku W. Performance Evaluation of 2-D Adaptive Prediction Filters for Detection of Small Objects in Image Data [J], IEEE Transactions on Image Processing. Vol.2:pp.327-340,1993
[44]Bello M G. A Random-field Model-based Algorithm for Anomalous Complex Image Pixel Detection [J]. IEEE Transactions on Image Processing, Vol.1(2): pp.186-196,1992
[45]Therrien C W, Quatieri T F, Dudgeon D E. Statistical model-based algorithms for image analysis [J]. IEEE Transactions On Acoustics, Speech and Signal Processing, Vol.74(4):pp.532-551,1986
[46]P.B. Chapple, D.C. Bertilone. R.S. Caprari, et al. Target detection in infrared and SAR terrain images using a non-Gaussian stochastic model [C]. Proc. SPIE, Vol.3699: pp.122-132,1999
[47]Denney B S, Figueiredo R J P. Optimal Point Target Detection Using Adaptive Auto Regressive Background Prediction [C]. Proc. SPIE, Vol.4048:pp.46-57, 2000
[48]李正周,董能力,金钢.起伏背景下红外运动点目标检测算法研究[J].红外与激光工程,Vol31(5):pp.410-414,2002
[49]向健勇,徐军,刘燕等.背景预测法检识空中红外弱小目标[J].激光与红外,Vol.27(4):205-208.1997
[50]李吉成,沈振康,鲁新平等.基于背景杂波自适应预测的红外弱小目标检测[J].激光与红外,Vol.34(6):pp.478-480,2004
[51]David S. K. Chan. David A. Langan, Daniel A. Staver. Spatial processing techniques for the detection of small targets in IR clutter [C]. Proc. SPIE, Vol.1305: pp.53-62,1990
[52]Ruiming Liu. Lei Yang, Fanglin Wang. Automatic extraction of infrared small target based on support vector regression and adaptive region growing algorithm [J]. Optical Engineering. Vol.46(4):046402,2007
[53]Leung H, Young A. Small target detection in clutter using recursive nonlinear prediction [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.36(2): pp.713-718,2000
[54]Leonov S. Nonparametric methods for clutter removal [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.37(3):pp.832-848,2001
[55]Sang Hongshi, Shen Xubang, Chen Chaoyang. Architecture of a configurable 2-D adaptive filter used for small object detection and digital image processing [J]. Optical Engineering, Vol.42(8):pp.2182-2189,2003
[56]刘志军,陈朝阳,沈绪榜等.基于卡尔曼滤波器的背景抑制及小目标检测[J].华中科技大学学报,Vol.32(12):pp.7-9,2004
[57]徐军,向健华,梁昌洪.最大化背景模型用于检测红外图像中的弱小目标[J].光子学报,Vol.31(12):pp.1483-1486,2002
[58]Bae Tae-Wuk, Kim Young-Choon, Ahn Sang-Ho, et al. An efficient two-dimensional least mean square (TDLMS) based on block statistics for small target detection [J]. Journal of Infrared, Millimeter, and Terahertz Waves, Vol.30(10):pp.1092-1101,2009
[59]何伟,晋兆虎,张玲.一种改进的利用背景检测弱小目标的方法[J].重庆大学学报,Vol.28(7):pp.64-66,2005
[60]陈振学,汪国有.基于自适应背景预测的红外弱小目标检测算法[J].激光与红外,Vol.35(8):pp.608-610,2005
[61]陈玉丹.基于最大化背景预测的图像预处理技术研究[J].红外技术,Vol.28(12):pp.683-685,2006
[62]陈振学,汪国有,马于涛等.基于均值反差滤波的红外小目标检测算法[J].武汉大学学报,Vol.32(6):pp.560-563,2007
[63]朱斌,樊祥,马东辉.自适应背景预测技术在红外弱小目标检测中的应用[J].红外,Vol.28(6):pp.21-24,2007
[64]Yanfeng Gu, Chen Wang, BaoXue Liu. A Kernel-Based Nonparametric Regression Method for Clutter Removal in Infrared Small-Target Detection Applications [J]. IEEE Geoscience and Remote Sensing Letters, Vol.7(3):pp.469-473,2010
[65]Lei Yang, Jie Yang, Ningsong Peng, et al. Weighted information entropy:a method for estimating the complex degree of infrared images'background [C]. ICIAR, Vol.LNCS 3656:pp.215-222,2005
[66]廖斌,杨卫平,沈振康.基于多帧移位叠加的红外小目标检测方法[J].红外与激光工程,Vol.31(2):pp.150-153,2002
[67]Blostein S D, Huang T S. Detection of small moving objects in image sequences using multistage hypothesis testing [C].1988 International Conference on Acoustics, Speech, and Signal Processing, Vol.2:pp.1068-1071,1988
[68]Lavagetto F. Automatic target detection in infrared sequences through semantic labeling [C]. Proc. SPIE, Vol.1349:pp.476-485,1990
[69]Diani M, Corsini G, Baldacci A. Space-time processing for the detection of airborne targets in IR image sequences [C]. IEE Proceedings of Vision, Image and Signal Processing, Vol.148(3):pp.151-157,2001
[70]彭嘉雄,彭铁.弱目标检测的图像流法[J].红外与激光工程,Vol.25(4):pp.34-40, 1996
[71]Shibata T, Frei W. Hough transform for target detection in infrared imagery [C]. Proc. SPIE, Vol.281:pp.105-109,1981
[72]Collet C, Fablet R. Motion Estimation on Infrared Image Sequences [J]. Proc. SPIE, Vol.3699:pp.67-77,1999
[73]Roth M W. Neural networks for extraction of weak targets in high clutter environments [C]. IJCNN International Joint Conference on Neural Networks, pp.275-282, 1989
[74]Thompson W B, Lechleider P, Stuck E. R. Detecting moving objects using the rigidity constraint [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15(2):pp.162-166,1993
[75]Milanfar P. Two-dimensional matched filtering for motion estimation [J]. IEEE Transactions on Image Processing, Vol.8(3):pp.438-444.1999
[76]Vega L, Cagigal M P, Prieto P. On determining the velocity of a moving object from a series of photon-limited frames using a temporal Fourier transform [C]. Proc. SPIE, Vol.1954:pp.198-203,1993
[77]Grossman S B, Emmons R B. Performance analysis and size optimization of focal planes for point-source tracking algorithm applications [J]. Optical Engineering, Vol.23(2):pp.167-176,1984
[78]Chu P L. Optimal Projection for Multidimensional Signal Detection [J]. IEEE Transactions on acoustics speech and signal processing, Vol.36(5):pp.775-786, 1988
[79]张金锁,宋利权,靳民会等.基于带通滤波的红外小目标捕获算法研究[J].红外与激光工程,Vol.31(4):pp.294-296,2002
[80]Lion R, Azimi-Sadjadi M R. Dim target detection using high order correlation method [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.29(3): pp.841-856,1993
[81]Wei P, Zeidler J, Ku W. Analysis of multiframe target detecting using pixel statistics [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.31(1): pp.238-247,1995
[82]鲁林丽,丁明跃,刘小平.基于HMM的红外点目标序列图像扰动补偿方法[J].红外与激光工程Vol.29(4):pp.30-33,2000
[83]于素芬,周洪武,柳毅.红外序列图像运动小目标检测算法[J].红外技术,Vol.27(3):pp.232-234,2005
[84]Gomarasca M A, Strobelt S, Zilioli E. Small-target detection with hyperdimensional data [C]. Proc. SPIE, Vol.2960:pp.212-218,1996
[85]Zavaljevski A, Dhawan A P, Kelch D J, et al. Adaptive hyperspectral small-target detection [C]. Proc. SPIE, Vol.2661:pp.118-128,1996
[86]Hoff L E, Evans J R, Bunney L. Detection of targets in terrain clutter by using multispectral infrared image processing [C]. Proc. SPIE, Vol.1481:pp.98-109, 1991
[87]Hoff L E, Winter E M. Demonstration of clutter reduction and aircraft detection in multi-spectral data [C]. Proc. SPIE, Vol.1698:pp.131-142,1992
[88]Yan Xiong, Jiaxiong Peng, Mingyue Ding, et al. An extended track-before-detect algorithm for infrared target detection [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.33(3):pp.1087-1092,1997
[89]David S. Chan, David A. Langan, Daniel A. Staver. Spatial-processing techniques for the detection of small targets in IR clutter [C]. Proc. SPIE Vol.1305:pp.53-62, 1990
[90]Yuan Cao, Ruiming Liu, Jie Yang. Small target detection using two-dimensional least mean square (TDLMS) filter based on neighborhood analysis [J]. Int J Infrared Milli Waves, Vol.29(2):pp.188-200,2008
[91]J. T. Barnett, B. D. Billard, Changta Lee, Nonlinear morphological processors for point-target detection versus an adaptive linear spatial filter-a performance comparison [C], Proc. SPIE Vol.1954:pp.12-24,1993
[92]S. Lashansky, N. Ben-Yosef, A. Weitz. Segmentation and statistical analysis of ground-based infrared cloudy sky images. Optical Engineering, Vol.31: pp.1057-1063,1992
[93]I. S. Reed, R. M. Gagliardi, H. M. Shao. Application of three-dimensional filtering to moving target detection [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.AES-19(6):pp.898-905,1983
[94]J. Silverman, J. M. Mooney, C. E. Caefer. Tracking point target in cloud clutter [C]. Proc. SPIE, Vol.3061:pp.496-507,1997
[95]A. P. Tzannes, D. H. Brooks. Detecting small moving objects using temporal hypothesis testing [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.38(2):pp.570-586,2002
[96]Lim Eng Thiam, Shue Louis, V Ronda. Adaptive mean and variance filter for detection of dim point-like targets [C]. Proc. SPIE, Vol.4728:pp.492-502,2002
[97]张兵,卢焕章.基于像素时域廓线分析的序列图像中弱小目标检测算法[J].中国图象图形学报,Vol.10(10):pp.1293-1298,2005
[98]Delian Liu, Jianqi Zhang, Weike Dong. Temporal profile based small moving target detection algorithm in Infrared image sequences [J]. International Journal of Infrared and Millimeter Waves, Vol.28(5):pp.373-381,2007
[99]王博,刘德连,张建奇.基于背景移除的时域目标检测[J].通信学报,Vol.30(7): pp.67-72,2009
[100]聂洪山.强起伏条件下红外小目标检测算法研究[D].国防科技大学博士学位论文,2004
[101]A. P. Tzannes, Detection of small targets in infrared image sequences containing evolving cloud clutter [D]. Ph. D. dissertation, Northeastern University, Boston, MA,1998
[102]A. P. Tzannes, D. H. Brooks. Temporal filters for point target detection in IR imagery [C]. Proc. SPIE, Vol.3061:pp.508-520.1997
[103]A. P. Tzannes, D. H. Brooks. Detecting small moving objects using temporal hypothesis testing [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.38(2):pp.570-586,2002
[104]Gan Wang, R. M. Inigo, E. S. Mcvey. A single-pixel target detection & tracking system [C]. IEEE Inter. Confer. On Pattern Recognition, pp.99-103,1990
[105]彭嘉雄,周文琳.红外背景抑制与小目标分割检测[J].电子学报,Vol.27:pp.47-51,1999
[106]刘刚,梁晓庚.基于小波变换和管道滤波的红外空中小目标检测[J].计算机工程与应用,Vol.47:pp.198-201,2011
[107]连可,王厚军,李丹.基于红外目标局部灰度特性分析的管道滤波算法[J].弹箭与制导学报,Vol.31:pp.200-203,2011
[108]董维科,向健勇,袁胜春.一种红外弱小目标精跟踪方法,激光与红外,Vol.35(3)pp.184-186,2005
[109]Steven M. Kay. Fundamentals of statistical signal processing [M]. Publishing House of Electronics Industry, pp.502-512,2003
[2]M. Diani, N. Acito, G. Corsini. Airborne threat detection in navy IRST systems [J]. IEE Proc., Vis. Image Signal Process. Vol.152:pp.45-51,2005
[3]方斌,陈天如,空空导弹红外成像制导关键技术分析[J],红外技术,Vo1.25(4):pp.45-48,2003
[4]奚云,红外告警技术[J],红外,Vo1.23(5):pp.10-14,2002
[5]田裕鹏,红外检测与诊断技术,化学工业出版社,2006
[6]Osmer, S. R. Nash, L. Hover, G. L.; et, al. Preliminary Assessment of U.S. Coast Guard Short Range Recovery (SRR) Forward Looking Infrared (FLIR) System Small Target Detection Performance. Interim rept. Sep 81-May 82
[7]B.D. Billard, M. Arozullah, J.T. Barnett. Application of nonparametric detectors to point target detection in infrared clutter backgrounds [C]. Proc. SPIE, Vol.1096: pp.108-18,1989
[8]John F. Bronskill, John S.A. Hepburn, Wing K. Au. Knowledge-based approach to the detection, tracking and classification of target formations in infrared image sequences [C]. Proc. IEEE Comput Soc Conf Comput Vision Pattern Recognit, pp.153-158, 1989
[9]R. Guissin. Infrared target acquisition using an adaptive Difference-of-Boxes filter [C]. Proc. SPIE, Vol.1157:pp.59-68,1989
[10]曹原,杨杰,刘瑞明.基于邻域分析TDLMS滤波器的红外小目标检测[J].红外与毫米波学报,Vol.28(3):pp.235-240,2009
[11]Meng Li, Tianxu Zhang, Weidong Yang, et, al. Moving dim small target detection and estimation with three-dimensional double directional filter in IR cluttered background [J]. Optical Engineering, Vol.44(10):pp.107007-1-4,2005
[12]T. Soni, J.R. Zeidler, W.H. Ku. Recursive estimation techniques for detection of small objects in infrared image data [C]. IEEE Proceedings International Conference on Acoustics Speech and Signal Processing. Vol.3:pp.581-584,1992
[13]T. V. DeMars. Adaptive dim point target detection and tracking in infrared images. ADA2048452,1988
[14]Himayat N, Kassam S. A. A structure for adaptive order statistics filtering [J]. IEEE Transactions on Image Processing. Vol.3(3):pp.265-280,1994
[15]Kotropoulos C, Pitas I. Adaptive LMS L-filters for noise suppression in images [J]. IEEE Transactions on Image Processing. Vol.5(12):pp.1596-1609,1996
[16]Muller M. Saliency measures in cluttered IR images for ATR [J]. Proc. SPIE, Vol.3699:pp.150-154,1999
[17]Barnett J. Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds [C]. Proc. SPIE, Vol.1050:pp.10-18, 1989
[18]Nichols S A, Naylor R B. Reliable motion detection of small targets in video with low signal-to-clutter ratios [C]. Proceedings of IEEE Annual International Camahan Conference on Security Technology, Vol.2561:pp.447-456,1995
[19]Scharfand L L, Friedlander B. Matched subspace detectors [J]. IEEE Transactions on Signal Processing. Vol.42(8):pp.2146-2157,1994
[20]Tom V. T, Peli. T, Leung M. et al. Morphology-based algorithm for point target detection in infrared backgrounds [C]. Proc. SPIE, Vol.1954:pp.2-11,1993
[21]叶斌,彭嘉雄,卢汉清.基于顺序形态滤波的红外运动小目标检测[J].数据采集与处理,Vol.16(3):pp.315-319,2001
[22]叶斌,彭嘉雄.基于形态学Top-Hat算子的小目标检测方法[J].中国图像图形学报,Vol.9(7):pp.638-642.2002
[23]曾明,李建勋.基于自适应形态学Top-Hat滤波器的红外弱小目标检测方法[J].上海交通大学学报,Vol.40(1):pp.90-93,2006
[24]Jian-Nan Chi, Ping Fu, Dong-Shu Wang, et al. A detection method of infrared image small target based on order morphology transformation and image entropy difference [C]. IEEE Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vol.8:pp.5111-5116,2005
[25]Zaveri M A, Merchant S N, Desai U B. Arbitrary trajectories tracking using multiple model based particle filtering in infrared image sequence [C]. IEEE Proceedings. ITCC 2004. International Conference on Information Technology:Coding and Computing, Vol.1:pp.603-607,2004
[26]Matthews K E, Namazi N M. A Bayes Decision Test for Detecting Uncovered-Background and Moving Pixels in Image Sequences [J]. IEEE Transactions on Image Processing. Vol.7(5):pp.720-728,1998
[27]Tonissen S M, Bar-Shalom Y. Detection of Low SNR Moving Targets with Time Varying Amplitude [C]. Fourth International Symposium on Signal Processing and its Applications, Vol.1:pp.29-32,1996
[28]Silverman J, Mooney J M, Caefer C E. Temporal filters for tracking weak slow point targets in evolving cloud clutter [J]. Infrared Physics & Technology. Vol.37(6):pp.695-710,1996
[29]Robin N. S, He H. Wavelet transform methods for object detection and recovery [J]. IEEE Transactions on Image Processing. Vol.6(5):pp.724-735,1997
[30]李国宽,彭嘉雄.基于小波变换的红外成像弱小目标检测方法[J].华中理工大学学报,Vo1.28(5)：69-71,2000
[31]柳钢,翟林培,贾新宇等.采用小波能量方法的海空背景中多目标检测与跟踪[J].光电工程,Vo1.31(11)：16-19,2004
[32]牟松涛,苏锦鑫,吴建东.基于小波变换的红外图像弱小目标检测研究[J].红外与激光工程,Vo1.33(5)：488-492,2004
[33]孙玉秋,田金文.基于双重能量累积的红外序列图像目标检测[J].长江大学学报,Vo1.2(1)：61-64,2005
[34]许彬,郑链,王克勇等.基于信号奇异性分析的小目标检测方法[J].红外技术,Vo1.27(3)：245-249,2005
[35]许彬,郑链,王克勇等.基于局部纹理分析的小目标检测方法[J].光电工程,Vo1.32(1)：40-42,2005
[36]彭嘉雄,周文琳.红外背景抑制与小目标分割检测[J],电子学报,Vo1.27(12):pp.47-51,1999
[37]Honggang Wu, Xiaofeng Li, Yuebin Chen, et al. Nonparametric Complex Background Prediction Algorithm Using FCM Cluttering for Dim Point Infrared Targets Detection [C].2006 International Conference on Communications, Circuits and Systems, Vol.1:pp.222-225,2006
[38]Zhenxue Chen, Guoyou Wang, Jianguo Liu, et al. Small Target Detection Algorithm Based on Average Absolute Difference Maximum and Background forecast [J]. International Journal of Infrared and Millimeter Waves. Vol.28(1): pp.87-97,2007
[39]Xu Kaiyu, Hu Wenhua, Zhou Weina, et al. Target Detection Based on The Artificial Neural Network Technology [C].9th International Conference on Control, Automation, Robotics and Vision, Vol.1:pp.1-5,2006
[40]Burton M, Benning C. Comparison of imaging infrared detection algorithms [C]. Proc. SPIE, Vol.302:26-32,1981
[41]Narayanan K A, Rosenfeld A. Image Smoothing by Local Use of Global Information [J]. IEEE Transaction on Systems, Man and Cybernetics, Vol.SMC-11(12):pp.826-831,1981
[42]王广君,田金文,柳健.基于局部熵的红外图像小目标检测[J].红外与激光工程,Vol.29(4):pp.26-29,2000
[43]Soni T, Zeidler J. Ku W. Performance Evaluation of 2-D Adaptive Prediction Filters for Detection of Small Objects in Image Data [J], IEEE Transactions on Image Processing. Vol.2:pp.327-340,1993
[44]Bello M G. A Random-field Model-based Algorithm for Anomalous Complex Image Pixel Detection [J]. IEEE Transactions on Image Processing, Vol.1(2): pp.186-196,1992
[45]Therrien C W, Quatieri T F, Dudgeon D E. Statistical model-based algorithms for image analysis [J]. IEEE Transactions On Acoustics, Speech and Signal Processing, Vol.74(4):pp.532-551,1986
[46]P.B. Chapple, D.C. Bertilone. R.S. Caprari, et al. Target detection in infrared and SAR terrain images using a non-Gaussian stochastic model [C]. Proc. SPIE, Vol.3699: pp.122-132,1999
[47]Denney B S, Figueiredo R J P. Optimal Point Target Detection Using Adaptive Auto Regressive Background Prediction [C]. Proc. SPIE, Vol.4048:pp.46-57, 2000
[48]李正周,董能力,金钢.起伏背景下红外运动点目标检测算法研究[J].红外与激光工程,Vol31(5):pp.410-414,2002
[49]向健勇,徐军,刘燕等.背景预测法检识空中红外弱小目标[J].激光与红外,Vol.27(4):205-208.1997
[50]李吉成,沈振康,鲁新平等.基于背景杂波自适应预测的红外弱小目标检测[J].激光与红外,Vol.34(6):pp.478-480,2004
[51]David S. K. Chan. David A. Langan, Daniel A. Staver. Spatial processing techniques for the detection of small targets in IR clutter [C]. Proc. SPIE, Vol.1305: pp.53-62,1990
[52]Ruiming Liu. Lei Yang, Fanglin Wang. Automatic extraction of infrared small target based on support vector regression and adaptive region growing algorithm [J]. Optical Engineering. Vol.46(4):046402,2007
[53]Leung H, Young A. Small target detection in clutter using recursive nonlinear prediction [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.36(2): pp.713-718,2000
[54]Leonov S. Nonparametric methods for clutter removal [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.37(3):pp.832-848,2001
[55]Sang Hongshi, Shen Xubang, Chen Chaoyang. Architecture of a configurable 2-D adaptive filter used for small object detection and digital image processing [J]. Optical Engineering, Vol.42(8):pp.2182-2189,2003
[56]刘志军,陈朝阳,沈绪榜等.基于卡尔曼滤波器的背景抑制及小目标检测[J].华中科技大学学报,Vol.32(12):pp.7-9,2004
[57]徐军,向健华,梁昌洪.最大化背景模型用于检测红外图像中的弱小目标[J].光子学报,Vol.31(12):pp.1483-1486,2002
[58]Bae Tae-Wuk, Kim Young-Choon, Ahn Sang-Ho, et al. An efficient two-dimensional least mean square (TDLMS) based on block statistics for small target detection [J]. Journal of Infrared, Millimeter, and Terahertz Waves, Vol.30(10):pp.1092-1101,2009
[59]何伟,晋兆虎,张玲.一种改进的利用背景检测弱小目标的方法[J].重庆大学学报,Vol.28(7):pp.64-66,2005
[60]陈振学,汪国有.基于自适应背景预测的红外弱小目标检测算法[J].激光与红外,Vol.35(8):pp.608-610,2005
[61]陈玉丹.基于最大化背景预测的图像预处理技术研究[J].红外技术,Vol.28(12):pp.683-685,2006
[62]陈振学,汪国有,马于涛等.基于均值反差滤波的红外小目标检测算法[J].武汉大学学报,Vol.32(6):pp.560-563,2007
[63]朱斌,樊祥,马东辉.自适应背景预测技术在红外弱小目标检测中的应用[J].红外,Vol.28(6):pp.21-24,2007
[64]Yanfeng Gu, Chen Wang, BaoXue Liu. A Kernel-Based Nonparametric Regression Method for Clutter Removal in Infrared Small-Target Detection Applications [J]. IEEE Geoscience and Remote Sensing Letters, Vol.7(3):pp.469-473,2010
[65]Lei Yang, Jie Yang, Ningsong Peng, et al. Weighted information entropy:a method for estimating the complex degree of infrared images'background [C]. ICIAR, Vol.LNCS 3656:pp.215-222,2005
[66]廖斌,杨卫平,沈振康.基于多帧移位叠加的红外小目标检测方法[J].红外与激光工程,Vol.31(2):pp.150-153,2002
[67]Blostein S D, Huang T S. Detection of small moving objects in image sequences using multistage hypothesis testing [C].1988 International Conference on Acoustics, Speech, and Signal Processing, Vol.2:pp.1068-1071,1988
[68]Lavagetto F. Automatic target detection in infrared sequences through semantic labeling [C]. Proc. SPIE, Vol.1349:pp.476-485,1990
[69]Diani M, Corsini G, Baldacci A. Space-time processing for the detection of airborne targets in IR image sequences [C]. IEE Proceedings of Vision, Image and Signal Processing, Vol.148(3):pp.151-157,2001
[70]彭嘉雄,彭铁.弱目标检测的图像流法[J].红外与激光工程,Vol.25(4):pp.34-40, 1996
[71]Shibata T, Frei W. Hough transform for target detection in infrared imagery [C]. Proc. SPIE, Vol.281:pp.105-109,1981
[72]Collet C, Fablet R. Motion Estimation on Infrared Image Sequences [J]. Proc. SPIE, Vol.3699:pp.67-77,1999
[73]Roth M W. Neural networks for extraction of weak targets in high clutter environments [C]. IJCNN International Joint Conference on Neural Networks, pp.275-282, 1989
[74]Thompson W B, Lechleider P, Stuck E. R. Detecting moving objects using the rigidity constraint [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15(2):pp.162-166,1993
[75]Milanfar P. Two-dimensional matched filtering for motion estimation [J]. IEEE Transactions on Image Processing, Vol.8(3):pp.438-444.1999
[76]Vega L, Cagigal M P, Prieto P. On determining the velocity of a moving object from a series of photon-limited frames using a temporal Fourier transform [C]. Proc. SPIE, Vol.1954:pp.198-203,1993
[77]Grossman S B, Emmons R B. Performance analysis and size optimization of focal planes for point-source tracking algorithm applications [J]. Optical Engineering, Vol.23(2):pp.167-176,1984
[78]Chu P L. Optimal Projection for Multidimensional Signal Detection [J]. IEEE Transactions on acoustics speech and signal processing, Vol.36(5):pp.775-786, 1988
[79]张金锁,宋利权,靳民会等.基于带通滤波的红外小目标捕获算法研究[J].红外与激光工程,Vol.31(4):pp.294-296,2002
[80]Lion R, Azimi-Sadjadi M R. Dim target detection using high order correlation method [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.29(3): pp.841-856,1993
[81]Wei P, Zeidler J, Ku W. Analysis of multiframe target detecting using pixel statistics [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.31(1): pp.238-247,1995
[82]鲁林丽,丁明跃,刘小平.基于HMM的红外点目标序列图像扰动补偿方法[J].红外与激光工程Vol.29(4):pp.30-33,2000
[83]于素芬,周洪武,柳毅.红外序列图像运动小目标检测算法[J].红外技术,Vol.27(3):pp.232-234,2005
[84]Gomarasca M A, Strobelt S, Zilioli E. Small-target detection with hyperdimensional data [C]. Proc. SPIE, Vol.2960:pp.212-218,1996
[85]Zavaljevski A, Dhawan A P, Kelch D J, et al. Adaptive hyperspectral small-target detection [C]. Proc. SPIE, Vol.2661:pp.118-128,1996
[86]Hoff L E, Evans J R, Bunney L. Detection of targets in terrain clutter by using multispectral infrared image processing [C]. Proc. SPIE, Vol.1481:pp.98-109, 1991
[87]Hoff L E, Winter E M. Demonstration of clutter reduction and aircraft detection in multi-spectral data [C]. Proc. SPIE, Vol.1698:pp.131-142,1992
[88]Yan Xiong, Jiaxiong Peng, Mingyue Ding, et al. An extended track-before-detect algorithm for infrared target detection [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.33(3):pp.1087-1092,1997
[89]David S. Chan, David A. Langan, Daniel A. Staver. Spatial-processing techniques for the detection of small targets in IR clutter [C]. Proc. SPIE Vol.1305:pp.53-62, 1990
[90]Yuan Cao, Ruiming Liu, Jie Yang. Small target detection using two-dimensional least mean square (TDLMS) filter based on neighborhood analysis [J]. Int J Infrared Milli Waves, Vol.29(2):pp.188-200,2008
[91]J. T. Barnett, B. D. Billard, Changta Lee, Nonlinear morphological processors for point-target detection versus an adaptive linear spatial filter-a performance comparison [C], Proc. SPIE Vol.1954:pp.12-24,1993
[92]S. Lashansky, N. Ben-Yosef, A. Weitz. Segmentation and statistical analysis of ground-based infrared cloudy sky images. Optical Engineering, Vol.31: pp.1057-1063,1992
[93]I. S. Reed, R. M. Gagliardi, H. M. Shao. Application of three-dimensional filtering to moving target detection [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.AES-19(6):pp.898-905,1983
[94]J. Silverman, J. M. Mooney, C. E. Caefer. Tracking point target in cloud clutter [C]. Proc. SPIE, Vol.3061:pp.496-507,1997
[95]A. P. Tzannes, D. H. Brooks. Detecting small moving objects using temporal hypothesis testing [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.38(2):pp.570-586,2002
[96]Lim Eng Thiam, Shue Louis, V Ronda. Adaptive mean and variance filter for detection of dim point-like targets [C]. Proc. SPIE, Vol.4728:pp.492-502,2002
[97]张兵,卢焕章.基于像素时域廓线分析的序列图像中弱小目标检测算法[J].中国图象图形学报,Vol.10(10):pp.1293-1298,2005
[98]Delian Liu, Jianqi Zhang, Weike Dong. Temporal profile based small moving target detection algorithm in Infrared image sequences [J]. International Journal of Infrared and Millimeter Waves, Vol.28(5):pp.373-381,2007
[99]王博,刘德连,张建奇.基于背景移除的时域目标检测[J].通信学报,Vol.30(7): pp.67-72,2009
[100]聂洪山.强起伏条件下红外小目标检测算法研究[D].国防科技大学博士学位论文,2004
[101]A. P. Tzannes, Detection of small targets in infrared image sequences containing evolving cloud clutter [D]. Ph. D. dissertation, Northeastern University, Boston, MA,1998
[102]A. P. Tzannes, D. H. Brooks. Temporal filters for point target detection in IR imagery [C]. Proc. SPIE, Vol.3061:pp.508-520.1997
[103]A. P. Tzannes, D. H. Brooks. Detecting small moving objects using temporal hypothesis testing [J]. IEEE Transactions on Aerospace and Electronic Systems, Vol.38(2):pp.570-586,2002
[104]Gan Wang, R. M. Inigo, E. S. Mcvey. A single-pixel target detection & tracking system [C]. IEEE Inter. Confer. On Pattern Recognition, pp.99-103,1990
[105]彭嘉雄,周文琳.红外背景抑制与小目标分割检测[J].电子学报,Vol.27:pp.47-51,1999
[106]刘刚,梁晓庚.基于小波变换和管道滤波的红外空中小目标检测[J].计算机工程与应用,Vol.47:pp.198-201,2011
[107]连可,王厚军,李丹.基于红外目标局部灰度特性分析的管道滤波算法[J].弹箭与制导学报,Vol.31:pp.200-203,2011
[108]董维科,向健勇,袁胜春.一种红外弱小目标精跟踪方法,激光与红外,Vol.35(3)pp.184-186,2005
[109]Steven M. Kay. Fundamentals of statistical signal processing [M]. Publishing House of Electronics Industry, pp.502-512,2003
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |