复杂背景抑制及弱小目标检测算法研究
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摘要
红外预警系统依靠被动地接受红外辐射实现检测、识别和跟踪来袭目标,相比雷达预警系统,它所具有的隐蔽性好、角分辨率高、抗电磁干扰能力强等优点,已受到各国军方的高度重视。由于红外预警系统还具备体积小、重量轻、机动性强等优点,它已逐渐成为现代化信息对抗体系中的一个重要组成部分。随着导弹、飞机等进攻性兵器速度的不断提高,要求红外预警系统能在较远的距离处搜索到来袭目标,以为我方对抗系统提供足够的预警时间。然而,当目标较远时,系统接收的是一种复杂背景中的微弱信号,即其不仅背景复杂、目标信号微弱、信噪比低,而且尺寸小、无形状、纹理信息,很难将其从背景中检测出来,以致如何实现复杂背景下红外弱小目标的可靠检测,成了当今国内外同仁广为研究的热门课题,它是一个具有重要理论意义和工程应用价值的研究课题。
本文主要研究天空复杂背景下的红外弱小目标检测问题,首先分析了红外图像中弱小目标及其背景特性,然后利用矩阵论、仿生学理论、多尺度几何分析、局部滤波、非线性滤波等理论工具,研究了相应的算法,主要包括红外弱小目标背景抑制和红外弱小目标多帧检测两方面的算法。本文主要研究成果有：
1、提出了两种背景抑制算法。针对天空中可能出现的大面积云层背景,提出了基于奇异值分解的人工蚁群背景抑制算法。该算法利用图像矩阵中奇异值在代数和几何上的不变性原理,对图像进行奇异值分解,用人工蚁群对分解结果优化。实验证明,该算法能有效抑制复杂多变的云层背景,提高了图像的信噪比和对比度,且适应性强,但是运算量偏大。为提高算法的实时性提出了一种基于多尺度奇异值分解背景抑制算法,该算法利用atrous小波的多尺度和平移不变性等优势,对小波分解后的子带进行特征值截断来调整系数,达到了抑制背景的目的。仿真结果表明该算法对一般的背景结构都能取得较好的抑制效果,有效地增强了目标信号。
2、针对天空背景中可能出现的结构化背景和强起伏云层,提出了一种改进的双边滤波算法和基于双边滤波的非抽样轮廓波背景抑制算法。利用目标和背景与其周围邻域相关性差异的特点,对像素的灰度和几何结构与其局部邻域之间的关系进行分析,提出了一种改进的双边滤波算法。该算法通过对背景图像的预测实现目标与背景的分离。利用非抽样轮廓波变换所具有的多尺度、多方向和平移不变性特点,采用双边滤波对各子带系数在空间距离、几何结构和灰度关系进行调整,提出了一种基于双边滤波的非抽样轮廓波背景抑制算法。实验证明,所提算法能对空空系统中的结构化背景进行有效抑制,提高图像整体的信噪比,使红外图像中复杂背景的抑制效果得到很大的提高。
3、提出了两种基于序列图像处理的弱小目标检测算法。利用目标图像的帧问相关特性和运动轨迹的连续性,构造了相关系数,针对机动性弱的常规线性运动目标,提出了一种基于卡尔曼滤波的帧间相关检测算法。当目标机动性较大时,其运动模型具有较强的非线性,针对这种情况提出了Unsecured粒子滤波的序列检测算法,实现了对该类目标的快速准确检测,并将该算法成功地应用于红外预警系统中。仿真结果表明,两种算法在不同运动模型下对目标的检测效果更加准确,减少了目标检测过程中虚假目标和漏检目标的数目,提高了对序列图像中弱小目标的检测能力。
4、红外弱小目标检测算法性能分析。红外弱小目标检测算法的性能与多种因素有关,就图像质量而言其评价指标包括信噪比和对比度等,就检测要求而言其评价指标包括检测概率和虚警概率等。本文对上述几个重要指标进行了综合、详细地分析,从理论上阐述了虚警概率和检测门限之间的关系、检测概率和信噪比之间的关系以及检测概率和虚警概率之间的关系。从工程实际出发,详细地分析了ROC曲线对于红外弱小目标检测算法性能评估的重要意义。利用仿真的序列图像对算法进行性能评估校验,给出了本文所提算法与经典算法比较的ROC曲线。
Infrared Surveillance System (IRSS) is used for target detection, recognition and trackion which employ electro-optical detectors receiving infrared emission passively. IRSS has advantages of high resolution, invisibility, fine anti-jamming ability, small size, lightweight and high maneuverability. For these advantages, IRSS becomes an important part of modern information confrontation system, and obtains more attention in recent years. As the speed of missiles and planes increase, IRSS is required to meet the demand of detecting long distance targets to ensure enough pre-alarm time. However, it is difficult to separate targets from background because of its small size, usually occupying only several pixels, no shape and texture information and so on. Therefore, how small and dim targets can be reliably detected in complex background has become a hot research issue and has important theoretical and practical value.
Aiming at the problem of small and dim target detection in complex sky background, the characteristics of target and background in infrared images are analyzed in this dissertation. Singular value decomposition (SVD) theory, ant colony system (ACS), multi-scale geometry analysis, bilateral filter and non-linear filter are deeply studied and used for infrared dim and small target detection. This study mainly focuses on background suppression algorithm for infrared small target and multi-frame infrared small target detection algorithm.
1. Aiming at the case that infrared images contain highlighted and large area background, two background suppression algorithms are proposed. According to the singular value with invariance principle in the algebra and geometry, a background suppression algorithm based on SVD and ACS is proposed to suppress complex sky background. The experimental results show that the algorithm can restrain the complex and various background structures, improve the SNR and contrast of image, but it has heavy computation. So a background suppression algorithm based on multi-scale SVD is developed to make it real-time implemention. According to the stationary wavelet characteristics with multi-scale and translation invariance, the algorithm suppress the background based on singularity value decomposition which adjusted the stationary wavelet sub-bands coefficients with truncated eigenvalue. Experimental results demonstrate that the algorithm can suppress the background and enhance the target signal effectively.
2. In order to suppress strong undulant background with complex texture effectively, two background suppression algorithms are developed based on new bilateral filtering. Through analyzing the relationship between each pixel with gray level, geometric structure and local neighborhood pixel, an improved bilateral filter algorithm is proposed, which can separate targets from background with prediction of the background image. By the advantage of the nonsubsampled contourlet transform (NSCT) characteristics with multi-scale, multi-directional and translation invariance. A new infrared dim target background suppression algorithm based on bilateral filter is proposed which adjusted the NSCT sub-bands coefficients with local neighborhood pixel in spatial distances, geometric structure and intensity. Experimental results show these algorithms can restrain complex background for surface-sky detection system effectively.
3. Two dim and small target detection algorithms are proposed based on the sequence image. Using inter-frame correlation and motion continuity of target, as well as the constructed gray correlation coefficient, a dim target detection algorithm based on Kalman filter is proposed. Since the actual target usually has high maneuverability, which means it has strong non-linear motion, so a unsecured particle filter for sequence detection algorithm is developed. Experimental results demonstrate that new algorithms can detect moving target under different motion model accurately, reduce the number of false targets and missed targets, and improve the detection capability of dim and small target in image sequences.
4. The performance of dim and small target detection algorithm is also studied in this dissertation. The performance of dim and small target detection algorithm is associated with many factors. As image quality is concerned, SNR, contrast ratio etc are involved as evaluation indicators. As for detection requirement, detection probability, false alarm probability etc are involved. In this study, the relationship between the false alarm probability and detection threshold is studied deeply, so as the relationship between detection probability and SNR, the detection probability and the false alarm probability. The ROC curves for performance evaluation in infrared dim and small target detection are also given. Simulation experiments evaluate the performances of the proposed algorithms using the ROC curves, which proves that the algorithms proposed are more efficient compared with the classical algorithms.
本文主要研究天空复杂背景下的红外弱小目标检测问题,首先分析了红外图像中弱小目标及其背景特性,然后利用矩阵论、仿生学理论、多尺度几何分析、局部滤波、非线性滤波等理论工具,研究了相应的算法,主要包括红外弱小目标背景抑制和红外弱小目标多帧检测两方面的算法。本文主要研究成果有：
1、提出了两种背景抑制算法。针对天空中可能出现的大面积云层背景,提出了基于奇异值分解的人工蚁群背景抑制算法。该算法利用图像矩阵中奇异值在代数和几何上的不变性原理,对图像进行奇异值分解,用人工蚁群对分解结果优化。实验证明,该算法能有效抑制复杂多变的云层背景,提高了图像的信噪比和对比度,且适应性强,但是运算量偏大。为提高算法的实时性提出了一种基于多尺度奇异值分解背景抑制算法,该算法利用atrous小波的多尺度和平移不变性等优势,对小波分解后的子带进行特征值截断来调整系数,达到了抑制背景的目的。仿真结果表明该算法对一般的背景结构都能取得较好的抑制效果,有效地增强了目标信号。
2、针对天空背景中可能出现的结构化背景和强起伏云层,提出了一种改进的双边滤波算法和基于双边滤波的非抽样轮廓波背景抑制算法。利用目标和背景与其周围邻域相关性差异的特点,对像素的灰度和几何结构与其局部邻域之间的关系进行分析,提出了一种改进的双边滤波算法。该算法通过对背景图像的预测实现目标与背景的分离。利用非抽样轮廓波变换所具有的多尺度、多方向和平移不变性特点,采用双边滤波对各子带系数在空间距离、几何结构和灰度关系进行调整,提出了一种基于双边滤波的非抽样轮廓波背景抑制算法。实验证明,所提算法能对空空系统中的结构化背景进行有效抑制,提高图像整体的信噪比,使红外图像中复杂背景的抑制效果得到很大的提高。
3、提出了两种基于序列图像处理的弱小目标检测算法。利用目标图像的帧问相关特性和运动轨迹的连续性,构造了相关系数,针对机动性弱的常规线性运动目标,提出了一种基于卡尔曼滤波的帧间相关检测算法。当目标机动性较大时,其运动模型具有较强的非线性,针对这种情况提出了Unsecured粒子滤波的序列检测算法,实现了对该类目标的快速准确检测,并将该算法成功地应用于红外预警系统中。仿真结果表明,两种算法在不同运动模型下对目标的检测效果更加准确,减少了目标检测过程中虚假目标和漏检目标的数目,提高了对序列图像中弱小目标的检测能力。
4、红外弱小目标检测算法性能分析。红外弱小目标检测算法的性能与多种因素有关,就图像质量而言其评价指标包括信噪比和对比度等,就检测要求而言其评价指标包括检测概率和虚警概率等。本文对上述几个重要指标进行了综合、详细地分析,从理论上阐述了虚警概率和检测门限之间的关系、检测概率和信噪比之间的关系以及检测概率和虚警概率之间的关系。从工程实际出发,详细地分析了ROC曲线对于红外弱小目标检测算法性能评估的重要意义。利用仿真的序列图像对算法进行性能评估校验,给出了本文所提算法与经典算法比较的ROC曲线。
Infrared Surveillance System (IRSS) is used for target detection, recognition and trackion which employ electro-optical detectors receiving infrared emission passively. IRSS has advantages of high resolution, invisibility, fine anti-jamming ability, small size, lightweight and high maneuverability. For these advantages, IRSS becomes an important part of modern information confrontation system, and obtains more attention in recent years. As the speed of missiles and planes increase, IRSS is required to meet the demand of detecting long distance targets to ensure enough pre-alarm time. However, it is difficult to separate targets from background because of its small size, usually occupying only several pixels, no shape and texture information and so on. Therefore, how small and dim targets can be reliably detected in complex background has become a hot research issue and has important theoretical and practical value.
Aiming at the problem of small and dim target detection in complex sky background, the characteristics of target and background in infrared images are analyzed in this dissertation. Singular value decomposition (SVD) theory, ant colony system (ACS), multi-scale geometry analysis, bilateral filter and non-linear filter are deeply studied and used for infrared dim and small target detection. This study mainly focuses on background suppression algorithm for infrared small target and multi-frame infrared small target detection algorithm.
1. Aiming at the case that infrared images contain highlighted and large area background, two background suppression algorithms are proposed. According to the singular value with invariance principle in the algebra and geometry, a background suppression algorithm based on SVD and ACS is proposed to suppress complex sky background. The experimental results show that the algorithm can restrain the complex and various background structures, improve the SNR and contrast of image, but it has heavy computation. So a background suppression algorithm based on multi-scale SVD is developed to make it real-time implemention. According to the stationary wavelet characteristics with multi-scale and translation invariance, the algorithm suppress the background based on singularity value decomposition which adjusted the stationary wavelet sub-bands coefficients with truncated eigenvalue. Experimental results demonstrate that the algorithm can suppress the background and enhance the target signal effectively.
2. In order to suppress strong undulant background with complex texture effectively, two background suppression algorithms are developed based on new bilateral filtering. Through analyzing the relationship between each pixel with gray level, geometric structure and local neighborhood pixel, an improved bilateral filter algorithm is proposed, which can separate targets from background with prediction of the background image. By the advantage of the nonsubsampled contourlet transform (NSCT) characteristics with multi-scale, multi-directional and translation invariance. A new infrared dim target background suppression algorithm based on bilateral filter is proposed which adjusted the NSCT sub-bands coefficients with local neighborhood pixel in spatial distances, geometric structure and intensity. Experimental results show these algorithms can restrain complex background for surface-sky detection system effectively.
3. Two dim and small target detection algorithms are proposed based on the sequence image. Using inter-frame correlation and motion continuity of target, as well as the constructed gray correlation coefficient, a dim target detection algorithm based on Kalman filter is proposed. Since the actual target usually has high maneuverability, which means it has strong non-linear motion, so a unsecured particle filter for sequence detection algorithm is developed. Experimental results demonstrate that new algorithms can detect moving target under different motion model accurately, reduce the number of false targets and missed targets, and improve the detection capability of dim and small target in image sequences.
4. The performance of dim and small target detection algorithm is also studied in this dissertation. The performance of dim and small target detection algorithm is associated with many factors. As image quality is concerned, SNR, contrast ratio etc are involved as evaluation indicators. As for detection requirement, detection probability, false alarm probability etc are involved. In this study, the relationship between the false alarm probability and detection threshold is studied deeply, so as the relationship between detection probability and SNR, the detection probability and the false alarm probability. The ROC curves for performance evaluation in infrared dim and small target detection are also given. Simulation experiments evaluate the performances of the proposed algorithms using the ROC curves, which proves that the algorithms proposed are more efficient compared with the classical algorithms.
引文
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