一种天地复杂背景下的红外弱小目标检测方法

宋敏敏; 王爽; 吕弢; 袁瑜键

红外技术, 2018, 40 (10): 996, 网络出版: 2018-12-17

一种天地复杂背景下的红外弱小目标检测方法

A Method for Infrared Dim Small Target Detection in Complex Scenes of Sky and Ground

论文大纲

宋敏敏 ^1,2,*王爽 ^1,2吕弢 ^1,2袁瑜键 ^1,2

作者单位

¹ 上海航天控制技术研究所，上海 201109

² 中国航天科技集团公司红外探测技术研发中心，上海 201109

红外弱小目标复杂场景形态学滤波 Canny算子边缘检测 infrared dim small target complex scenes morphology filter Canny operator edge detection

摘要

针对天地复杂背景下红外弱小目标的检测，提出了一种融合 top-hat变换、边缘检测的新方法。首先将改进的 top-hat变换应用于红外弱小目标图像，将图像中与目标特性相似的像素进行增强，并去除云层背景对目标的干扰，得到若干疑似目标的增强结果，并结合阈值分割方法对增强结果进行筛选，剔除大部分不符合弱小红外目标特性的背景干扰点。然后采用基于 Canny算子的边缘提取方法对原始图像中的天地线进行检测，并以天地分割线作为先验知识，去除形态学滤波后所增强的地物背景中的疑似目标，得到最终的真实目标的检测结果。通过实验得出，本文所提出的方法对于天地背景下的弱小红外目标具有良好的检测效果。

Abstract

A new method for infrared dim small target detection for images with complex scenes of sky and ground is proposed. Firstly, a modified top-hat transform is applied to the image of the infrared dim target, enhancing the target’s characteristic pixels. Cloud disturbances are removed, and several target candidate are picked out as the enhanced results. Threshold segmentation is used to separate the enhanced results and facilitate the removal of any without the infrared dim small target feature. The Canny edge detection method is the applied to recognize the horizon; this is taken as a prior to remove the suspected targets in the ground after the morphology filtering, leaving the real infrared dim target remaining for detection. The experimental results show that the method can effectively detect an infrared dim small target in complex scenes of sky and ground.

参考文献

[1] Vicotr T Tom, Tamar Peli, May Leung, et al. Morphology-based algorithm for point target detection in infrared background[C]//Proc. of SPIE, 1993, 1954(1): 2-11.

[2] 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, 2006, 40(1): 90-93.

ZENG Ming, LI Jianxun. The small target detection in infrared image based on adaptive morphological Top-Hat filter[J]. Journal of Shanghai Jiaotong University, 2006, 40(1): 90-93.

[3] John Barnett. Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds[C]//Proc. of SPIE, 1989, 1050(1): 10-18.

[4] Suyog D Deshpande, Meng H Er, Ronda Venkateswarlu, et al. Max-mean and max-median filters for detections of small target[J]. Proc of SPIE, 1999, 3809(1):74-83.

[5] 王丽荣. 基于小波变换的目标检测方法研究 [D].长春: 吉林大学, 2006.

WNANG Lirong. Research of Target Detection Based on Wavelet Transform[D]. Changchun: University of Jilin, 2006.

[6] Romain M, Davida J. Detection of targets in low resolution FLIR imagery using two dimension directional wavelets[C]//Proc. of SPIE, 1998, 3361: 510-515.

[7] 孙广富, 张兵, 卢焕章 . 基于窗口预测匹配的序列图像点目标轨迹检测算法[J].国防科技大学学报, 2004, 26(2): 25-29.

SUN Guangfu, ZHANG Bing, LU Huanzhang. The detection algorithm based on predicting-matching-window for moving point target trajectory in image sequences[J]. Journal of National University of Defense Technology, 2004, 26(2): 25-29.

[8] 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, 2002, 31(2): 150-153.

LIAO Bin, YANG Weiping, SHEN Zhenkang. Dim target detection algorithm based on multi-frame indexing accumlation[J]. Infrared and Laser Engineering, 2002, 31(2): 150-153.

[9] 吴一全. 基于 FLS-SVM背景预测的红外弱小目标检测 [J].光学学报, 2010, 30(10): 2806-2811.

WU Yiquan. Detection of small target infrared image based on background prediction by FLS-SVM[J]. Acta Optical Sinica, 2010, 30(10): 2806-2811.

[10] Won Y G, Gader P G, Coffield P D. Morphological shared-weight networks with applications to automatic target recognition[J]. Transactions on Neural Networks, 1997, 8(5): 1195-1203.

[11] Khan J F, Alam M S. Target detection in cluttered FLIR imagery using probabilistic neural networks[C]//Proc. of SPIE, 2005, 5807: 55-66.

[12] 刘源, 汤心溢, 李争. 基于新 Top-Hat变换局部对比度的红外小目标检测[J].红外技术, 2015, 37(7): 544-552.

LIU Yuan, TANG Xinyi, LI Zheng. A new top-hat local contrast based algorithm for infrared small target detection[J]. Infrared Technology, 2015, 37(7): 544-552.

[13] BAI Xiangzhi, ZHOU Fugen. Analysis of new top-hat transformation and the application for infrared dim small target detection[J]. Pattern Recognition, 2010, 43(6): 2145-2156.

[14] 王玲玲, 辛云宏 . 基于形态学与遗传粒子滤波器的红外小目标检测与跟踪算法[J].光子学报, 2013, 42(7): 849-856.

WANG Lingling, XIN Yunhong. A small IR target detecion and tracking algorithm based on morphological and genetic-partical filter[J]. Acta Photonica Snica, 2013, 42(7): 849-856.

[15] 高海峰, 张生伟, 聂青凤. 基于红外图像的天地线检测方法研究 [J]. 电光与控制, 2016, 23(7): 20-23.

GAO Haifeng, ZHANG Shengwei, NIE Qingfeng. Horizon detection based on imaging charateristic of infrared images[J]. Electronics Optics & Control, 2016, 23(7): 20-23.

[16] 安博文, 胡春暖, 刘杰, 等. 基于 Hough变换的海天线检测算法研究 [J].红外技术, 2015, 37(3): 196-199.

AN Bowen, HU Chunnuan, LIU Jie, et al. Study of sea-sky-line detection algorithm based on Hough transform[J]. Infrared Technology, 2015, 37(3): 196-199

[17] 张斌, 赛贺先. 基于 Canny算子的边缘提取改善方法 [J].红外技术, 2006, 28(3): 165-169.

ZHANG Bin, HE Saixian. Improved edge-detection method based on Canny algorithm[J]. Infrared Technology, 2006, 28(3): 165-169.

[18] 张腾飞, 张合新, 孟飞, 等. 基于改进 Canny的激光主动成像图像边缘检测算法研究[J].导航定位与授时, 2016, 3(6): 57-62.

ZHANG Tengfei, ZHANG Hexin, MENG Fei, et al. Laser active imaging image edge-detection algorithm based on improved Canny algorithm[J]. Navigation Positioning & Timing, 2016, 3(6): 57-62.

[19] 王鑫. 复杂背景下红外目标检测与跟踪算法研究[D].南京: 南京理工大学, 2010.

WANG Xin. Research on Infrared Target Detection and Tracking Algorithms under Complex Background[D]. Nanjing: University of Science and Technology, 2010.

宋敏敏, 王爽, 吕弢, 袁瑜键. 一种天地复杂背景下的红外弱小目标检测方法[J]. 红外技术, 2018, 40(10): 996. SONG Minmin, WANG Shuang, LYU Tao, YUAN Yujian. A Method for Infrared Dim Small Target Detection in Complex Scenes of Sky and Ground[J]. Infrared Technology, 2018, 40(10): 996.

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