红外目标检测
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摘要
本文在分析了红外图像中背景、小目标和噪声特点的基础上，进行了两方面的研究。第一个方面是从红外图像中小目标的特性出发，对图像进行高通滤波，得到主要包含小目标和噪声的图像。然后，采用本文提出的基于小波变换中软阈值去噪原理和相关去噪原理的检测方法检测出小目标。同时，仿真结果表明基于软阈值去噪原理的算法优于基于相关去噪原理的算法。第二个方面主要是通过抑制背景来检测小目标。这一方面又分为两种情况。第一种情况是针对连续的多帧图像。分别采用时域中值滤波器算法和最小平方中值滤波器算法抑制背景，得到由小目标引起的变化的部分。第二种情况是针对单帧图像，采用数学形态学抑制背景，得到保留小目标的图像。然后，对包含小目标信息的图像进行处理，从而检测出小目标。同时，仿真结果表明，数学形态学算法最优，时域中值滤波器算法次之，最小平方中值滤波器算法最差。
Based on the different characteristics of background, small targets and noise in infrared images, the research is conducted in two different ways in this paper. One is based on the characteristic of small targets. First, one image is filtered by a high pass filter and the image mainly including the small targets and noise are obtained. Then, using the algorithms with wavelet transformation presented in this paper, which are based on the soft-threshold denoising and the relevant denoising, the small target can be detected. At the same time, the simulation result shows that the performance of the algorithm that is based on the soft-threshold denoising is better than the algorithm that is based on the relevant denoising. The other is mainly depended on the background rejection to detect the small target. There are two different situations in this field. One aims at the image sequences. Using the LMedS (Least median of squares) algorithm and the temporal median filter algorithm independently, the background rejection can be realized, and the changed parts in the image sequences that are mainly caused by the small target are obtained. The other aims at one image. Using the mathematic morphology algorithm, the background rejection can be realized and the image mainly including the small target can be obtained. Then, the images that are obtained by the LMedS algorithm, the temporal median filter algorithm and the mathematic morphology algorithm are processed and the small target is detected. At the same time, the simulation result shows that the performance of the mathematic morphology algorithm is the best, the performance of the temporal median filter algorithm ranks second, and the performance of the LMedS algorithm is the worst.
Based on the different characteristics of background, small targets and noise in infrared images, the research is conducted in two different ways in this paper. One is based on the characteristic of small targets. First, one image is filtered by a high pass filter and the image mainly including the small targets and noise are obtained. Then, using the algorithms with wavelet transformation presented in this paper, which are based on the soft-threshold denoising and the relevant denoising, the small target can be detected. At the same time, the simulation result shows that the performance of the algorithm that is based on the soft-threshold denoising is better than the algorithm that is based on the relevant denoising. The other is mainly depended on the background rejection to detect the small target. There are two different situations in this field. One aims at the image sequences. Using the LMedS (Least median of squares) algorithm and the temporal median filter algorithm independently, the background rejection can be realized, and the changed parts in the image sequences that are mainly caused by the small target are obtained. The other aims at one image. Using the mathematic morphology algorithm, the background rejection can be realized and the image mainly including the small target can be obtained. Then, the images that are obtained by the LMedS algorithm, the temporal median filter algorithm and the mathematic morphology algorithm are processed and the small target is detected. At the same time, the simulation result shows that the performance of the mathematic morphology algorithm is the best, the performance of the temporal median filter algorithm ranks second, and the performance of the LMedS algorithm is the worst.
引文
[1] 韩客松. 复杂背景下红外点目标检测的预处理. 系统工程与电子技术. 2000,1,Vol.22,No.1. 52~54。
[2] Alexander Tartakovsky, Rudolf Blazek. Effective Adaptive Spatial-Temporal Technique for Clutter Rejection in IRST. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 2000,7,Vol.4048. 85-95。
[3] David L. Donoho. De-Nosising by Soft-Thresholding. IEEE Trans on Information Theory. 1995,5, Vol.41,No.3. 613-627。
[4] 赵瑞珍. 小波变换及其在信号去噪与重构中的应用. 西安电子科技大学硕士研究生学位论文. 2000。
[5] Paul L.Rosi, Tim Ellis. Image Difference Threshold Strategies and Shadow Detection. In proceedings of the 6th British Machine Vision Conference. 1995. 347-356。
[6] David Casasent, Anqi Ye. Detection Filters and Algorithm Fusion for ATR. IEEE Trans on Image Processing. 1997,1,Vol.6, No.1. 114-125。
[7] Sang Nong, Zhang Tianxu, Shi Weiqiang. Characteristics of Contrast and Application for Small Target Detection. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 1998,9,Vol.3373. 123-129。
[8] 陈海林. 基于小波变换的红外图像点目标检测. 西安电子科技大学硕士研究生学位论文. 2000。
[9] 杨卫平, 沈振康. 起伏背景下的自适应门限检测方法. 红外与毫米波学报. 1999,4,Vol.18, No.2. 120-124。
[10] 李斌, 彭嘉雄. 红外小目标图像的分割与聚类分析. 红外与激光工程. 2000,11,Vol.29,No.6. 60-63。
[11] 张弘, 赵保军, 毛二可等. 复杂背景下红外点目标的检测. 红外与激光工程. 2001,4,Vol.30,No.2. 96-98。
[12] Yi Sun. Tracking and Detection of Moving Point Targets In Noise Image Sequences by Local Maximum Likelihood. ICIP. 96,9. 799-802。
[13] 朱红, 赵亦工. 基于背景自适应预测的红外弱小目标运动检测. 红外与毫米波学报. 1999,8,Vol.18,No.4. 305-309。
[14] 彭嘉雄, 周文琳. 红外背景抑制与小目标分割检测. 电子学报. 1999,11,Vol.27,No.12. 47-51。
[15] 徐军, 向健勇, 林晓春等. 背景预测方法在空中红外弱小目标检测，识别中的
应用. 西安电子科技大学学报. 1998,8,Vol,25,No.4. 471-474。
[16] 李国宽, 彭嘉雄, 李红. 基于相量小波变换的小目标检测方法. 华中理工大学学报. 2000,1,Vol.28,No.1. 73-75。
[17] 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. 华中理工大学学报. 2000,5,Vol.28,No.5. 69-71。
[18] David P.Casasent, John-Scott Smokelin, Anqi Ye. Wavelet and Gabor Transforms for Detection. Optical Engineering. 1992,9,Vol.31,No.9. 1893-1898。
[19] Robin N. Strickland, Hee I Hahn. Wavlet Transform Methods for Object Detection and Recovery. IEEE Trans on Image Processing. 1997,5,Vol.6, No.5. 724-735。
[20] D. Davies, P. L. Palmer, M. Mirmehdi. Robust Tracker of Small, Fast-moving Low-contrast Tragets. In Proceedings of IX European Signal Processing Conference. 1998,9,Vol.3. 1545-1548。
[21] 李红艳, 吴成柯. 一种基于小波与遗传算法的小目标检测算法. 电子学报. 2001,4,Vol.29,No.4. 436-439。
[22] 张燕, 张桂林. 一种基于数学形态学的小目标检测方法. 数据采集与处理. 1997,11,Vol.12, No. 4 276-279。
[23] 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. 数据采集与处理. 2001,9,Vol.16,No.3. 315-319。
[24] Tom Victor T., Peli Tamar, Leung May etc. Morphology-Based Algorithm for Point Target Detection in Infrared Backgrounds. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 1993,10,Vol.1954. 2-11。
[25] Barnett John T., Billard Barton D., Lee Changta. Nonlinear Morphological Processors for Point-Target Detection Versus an Adaptive Linear Spatial Filter: A Performance Comparison. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 1993,10,Vol.1954. 12-24。
[26] Sang Nong, Zhang Tianxu, Wang Guoyou. Gray Scale Morphology for Small Object Detecion. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 1996,5,Vol.2759. 589-595。
[27] Anqi Ye, David Casasent. Morphological and Wavelet Transforms for Object Detection and Image Processing. Applied Optics. 1994,10,Vol.33,No.35. 8226-8239。
[28] 叶增军, 王江安, 阮玉等. 海空复杂背景下红外弱点目标的检测算法. 红外与毫米波学报. 2000,Vol.19,No.2. 121-124。
[29] 党莹, 翟林培. 基于小波多分辨力分析的低信杂比下点目标跟踪方法. 光电
工程. 2000,10,Vol.27.No.5. 47-51。
[30] 胡战虎. 贝叶斯估计和尺度空间滤波相结合的滤波方法. 系统工程与电子技术. 2001,Vol.23,No.4. 29-32。
[31] 潘泉, 戴冠中, 张洪才等. 基于阈值决策的子波域去噪方法. 电子学报. 1998,1,Vol.26,No.1. 115-117。
[32] 何焰兰, 苏勇, 高永楣. 一种自适应小波去噪算法. 电子学报. 2000,10,Vol.28,No.10. 138-140。
[33] 林哲民, 康学雷, 张立明. 在小波域中进行图像噪声方差估计的EM方法. 红外与毫米波学报. 2001,1,Vol.20,No.3. 199-202。
[34] 彭玉华. 小波变换与工程应用. 第一版. 北京: 科学出版社, 1999. 39-40。
[35] 崔屹. 图像处理与分析——数学形态学方法及应用. 第一版. 北京：科学出版社，2000。
[2] Alexander Tartakovsky, Rudolf Blazek. Effective Adaptive Spatial-Temporal Technique for Clutter Rejection in IRST. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 2000,7,Vol.4048. 85-95。
[3] David L. Donoho. De-Nosising by Soft-Thresholding. IEEE Trans on Information Theory. 1995,5, Vol.41,No.3. 613-627。
[4] 赵瑞珍. 小波变换及其在信号去噪与重构中的应用. 西安电子科技大学硕士研究生学位论文. 2000。
[5] Paul L.Rosi, Tim Ellis. Image Difference Threshold Strategies and Shadow Detection. In proceedings of the 6th British Machine Vision Conference. 1995. 347-356。
[6] David Casasent, Anqi Ye. Detection Filters and Algorithm Fusion for ATR. IEEE Trans on Image Processing. 1997,1,Vol.6, No.1. 114-125。
[7] Sang Nong, Zhang Tianxu, Shi Weiqiang. Characteristics of Contrast and Application for Small Target Detection. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 1998,9,Vol.3373. 123-129。
[8] 陈海林. 基于小波变换的红外图像点目标检测. 西安电子科技大学硕士研究生学位论文. 2000。
[9] 杨卫平, 沈振康. 起伏背景下的自适应门限检测方法. 红外与毫米波学报. 1999,4,Vol.18, No.2. 120-124。
[10] 李斌, 彭嘉雄. 红外小目标图像的分割与聚类分析. 红外与激光工程. 2000,11,Vol.29,No.6. 60-63。
[11] 张弘, 赵保军, 毛二可等. 复杂背景下红外点目标的检测. 红外与激光工程. 2001,4,Vol.30,No.2. 96-98。
[12] Yi Sun. Tracking and Detection of Moving Point Targets In Noise Image Sequences by Local Maximum Likelihood. ICIP. 96,9. 799-802。
[13] 朱红, 赵亦工. 基于背景自适应预测的红外弱小目标运动检测. 红外与毫米波学报. 1999,8,Vol.18,No.4. 305-309。
[14] 彭嘉雄, 周文琳. 红外背景抑制与小目标分割检测. 电子学报. 1999,11,Vol.27,No.12. 47-51。
[15] 徐军, 向健勇, 林晓春等. 背景预测方法在空中红外弱小目标检测，识别中的
应用. 西安电子科技大学学报. 1998,8,Vol,25,No.4. 471-474。
[16] 李国宽, 彭嘉雄, 李红. 基于相量小波变换的小目标检测方法. 华中理工大学学报. 2000,1,Vol.28,No.1. 73-75。
[17] 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. 华中理工大学学报. 2000,5,Vol.28,No.5. 69-71。
[18] David P.Casasent, John-Scott Smokelin, Anqi Ye. Wavelet and Gabor Transforms for Detection. Optical Engineering. 1992,9,Vol.31,No.9. 1893-1898。
[19] Robin N. Strickland, Hee I Hahn. Wavlet Transform Methods for Object Detection and Recovery. IEEE Trans on Image Processing. 1997,5,Vol.6, No.5. 724-735。
[20] D. Davies, P. L. Palmer, M. Mirmehdi. Robust Tracker of Small, Fast-moving Low-contrast Tragets. In Proceedings of IX European Signal Processing Conference. 1998,9,Vol.3. 1545-1548。
[21] 李红艳, 吴成柯. 一种基于小波与遗传算法的小目标检测算法. 电子学报. 2001,4,Vol.29,No.4. 436-439。
[22] 张燕, 张桂林. 一种基于数学形态学的小目标检测方法. 数据采集与处理. 1997,11,Vol.12, No. 4 276-279。
[23] 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. 数据采集与处理. 2001,9,Vol.16,No.3. 315-319。
[24] Tom Victor T., Peli Tamar, Leung May etc. Morphology-Based Algorithm for Point Target Detection in Infrared Backgrounds. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 1993,10,Vol.1954. 2-11。
[25] Barnett John T., Billard Barton D., Lee Changta. Nonlinear Morphological Processors for Point-Target Detection Versus an Adaptive Linear Spatial Filter: A Performance Comparison. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 1993,10,Vol.1954. 12-24。
[26] Sang Nong, Zhang Tianxu, Wang Guoyou. Gray Scale Morphology for Small Object Detecion. SPIE Proceedings: Signal and Data Proceeding of Small Targets. 1996,5,Vol.2759. 589-595。
[27] Anqi Ye, David Casasent. Morphological and Wavelet Transforms for Object Detection and Image Processing. Applied Optics. 1994,10,Vol.33,No.35. 8226-8239。
[28] 叶增军, 王江安, 阮玉等. 海空复杂背景下红外弱点目标的检测算法. 红外与毫米波学报. 2000,Vol.19,No.2. 121-124。
[29] 党莹, 翟林培. 基于小波多分辨力分析的低信杂比下点目标跟踪方法. 光电
工程. 2000,10,Vol.27.No.5. 47-51。
[30] 胡战虎. 贝叶斯估计和尺度空间滤波相结合的滤波方法. 系统工程与电子技术. 2001,Vol.23,No.4. 29-32。
[31] 潘泉, 戴冠中, 张洪才等. 基于阈值决策的子波域去噪方法. 电子学报. 1998,1,Vol.26,No.1. 115-117。
[32] 何焰兰, 苏勇, 高永楣. 一种自适应小波去噪算法. 电子学报. 2000,10,Vol.28,No.10. 138-140。
[33] 林哲民, 康学雷, 张立明. 在小波域中进行图像噪声方差估计的EM方法. 红外与毫米波学报. 2001,1,Vol.20,No.3. 199-202。
[34] 彭玉华. 小波变换与工程应用. 第一版. 北京: 科学出版社, 1999. 39-40。
[35] 崔屹. 图像处理与分析——数学形态学方法及应用. 第一版. 北京：科学出版社，2000。