摘要
空间观测的星空图像中,由于恒星距离较远在图像中仅占几个像素,且存在大量噪声,因此很多信噪比较低的弱小目标被淹没。在预处理环节应将其有效地检测出来,降低后续目标识别和跟踪的虚警率。首先对星空图像的噪声模型进行了分析,通过最小二乘拟合法得到图像背景参数。利用两次检验的方法,首先对目标进行第一次粗验,利用管道滤波的方法进行第二次确认,滤除噪声得到目标。最后通过能量累积的方法对原图像中弱小目标进行增强。仿真实验结果表明了算法的有效性。
Among images observed by systems in starry sky,because stars are far away from the systems,only several pixels in the images and much noise,lots of dim targets with lower signal-to-noise ratio are sub merged.So it should be detected effectively during pre-processing so as to reduce false alarm rate of identifica tion and tracking of subsequent targets.Firstly,noise models of the images are analyzed and image background parameters are got by the least squares fitting method.And then through secondary test method,targets are tested coarsely for the first time and pipeline filtering method is used for identification for the second time.So noise is filtered and the target is got.Finally,dim targets in original images are enhanced by energy accumulated method.Simulation results show that the algorithm is effective.
出处
《光电技术应用》
2013年第4期37-40,共4页
Electro-Optic Technology Application
基金
国家自然科学基金(51005242)
关键词
星空图像
弱小目标检测
最小二乘
管道滤波
能量累计
starry sky image
dim target detection
least squares
pipeline filter
energy accumulation
