| 基于局部特性实现单帧图像小目标检测的研究 |
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| 引用本文： | 吕建明,牛燕雄,刘海霞,杨露,许冰,张颖,牛海莎,刘雯文,李继扬.基于局部特性实现单帧图像小目标检测的研究[J].红外,2014,35(2):37-43. |
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| 作者姓名： | 吕建明 牛燕雄 刘海霞 杨露 许冰 张颖 牛海莎 刘雯文 李继扬 |
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| 作者单位： | 北京航空航天大学仪器科学与光电工程学院,北京航空航天大学仪器科学与光电工程学院,北京航空航天大学仪器科学与光电工程学院,北京航空航天大学仪器科学与光电工程学院,北京航空航天大学仪器科学与光电工程学院,北京航空航天大学仪器科学与光电工程学院,北京航空航天大学仪器科学与光电工程学院,北京航空航天大学仪器科学与光电工程学院,北京航空航天大学仪器科学与光电工程学院 |
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| 摘 要： | 以目标成像点的扩散理论为基础,建立小目标在空域上的灰度特性模型,分析目标、背景和噪声的基本特性。由形态学开闭运算得到各像素位置的灰度变化值,再根据此值确定潜在目标区域。研究了各潜在目标区域的多方向多级梯度特征,实现了单帧图像的小目标检测。研究结果表明,该方法能够有效抑制不均匀背景杂波,增强目标信号,提高单帧亮暗点目标的检测能力。对于信噪比为0.89的图像,可获得34.74的信噪比增益。
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| 关 键 词： | 小目标检测 背景杂波抑制 多方向多级梯度 |
| 收稿时间： | 2013/12/20 |
| 修稿时间： | 2013/12/27 0:00:00 |
Study of Small Target Detection in Single Frame Image Based on Local Characteristics |
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| LV Jianming,NIU Yanxiong,LIU Haixi,YANG Lu,XU Bing,ZHANG Ying,NIU Haish,LIU Wenwen and LI JiYang.Study of Small Target Detection in Single Frame Image Based on Local Characteristics[J].Infrared,2014,35(2):37-43. |
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| Authors: | LV Jianming NIU Yanxiong LIU Haixi YANG Lu XU Bing ZHANG Ying NIU Haish LIU Wenwen and LI JiYang |
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| Affiliation: | Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University,Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University,Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University,Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University,Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University,Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University,Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University,Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University,Dept. of Instrument Science and Opto-Electronics Engineering,Beihang University |
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| Abstract: | A grey level model of small targets in the spatial domain is established on the basis of the point spread theory in target imaging. The basic characteristics of the target, background and noise are analyzed. After the open and close morphologic operation is implemented, the grey level variation in each pixel position is derived. Thus, the potential target areas are determined. The multi-orientation and multi-degree gradient of each potential area are studied. The detection of small targets is implemented in a single frame. The result shows that this method can effectively suppress uneven background clutter, enhance target signals and improve the detection of bright and dark small targets in a single frame. For an image with a signal-to-noise ratio (SNR) of 0.89, a SNR gain of 34.74 can be obtained. |
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| Keywords: | small target detection background clutter suppression multi-orientation gradient and multi-degree |
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