形态学和小波域杂波抑制的微弱目标检测(英文)  

Dim Target Detection Based on Spatio-morphological and Wavelet Transform Clutter Suppression

作　　者：周宁[1] 李晓峰[1] 李在铭[1] 

机构地区：[1]电子科技大学通信与信息工程学院,成都610054

出　　处：《光电工程》2009年第11期25-28,共4页Opto-Electronic Engineering

基　　金：国家自然科学基金项目(60873263);国家863高技术项目(2009A01Z215)

摘　　要：本文提出了一种基于形态学和小波域杂波抑制的微弱目标检测方法,该方法将图像序列进行形态学tophat滤波,然后小波变换,再分别对各小波子带作平滑滤波,按各子带对滤波前后小波系数作差分运算,最后经过小波逆变换得到具有微弱目标的残差图像序列。用残差图像tophat结果估计目标潜在区域,在目标潜在域的约束下,对残差图像序列进行时空域数据融合,实现微弱运动目标的检测。仿真实验表明,该方法杂波抑制后残差图像具有很好的白高斯特性,且目标邻域信杂比(SCNR)的平均增益比图像空域平滑滤波和图像频域低通滤波等典型运算的SCNR平均增益有明显改善,目标检测算法在5帧图像集成时能稳定检测出微弱运动目标轨迹。The method of dim target detection based on spatio-morphological and wavelet transform clutter suppression is proposed. The image sequences are processed with spatial tophat filtering. The results are transformed into wavelet domain, and filtered by smooth filter in every wavelet belt, and the difference process is operated between the pre- and after filtering coefficients. The inverse wavelet transform is carried out to produce the residue image sequences with dim targets. The tophat filtered image is used to estimate the possible target support area. Under the restriction of the possible target area, the tempo-spatial data fusion is performed to detect the trajectories of dim targets. Simulation results show that the obtained residual images have very good white Gaussian normality, and the average gain of the target＇s neighborhood Signal-to-clutter-noise Ratio （SCNR） can be improved obviously compared with traditional image smooth filtering and frequency filtering algorithms. The trajectories of dim targets can be detected steadily with 5 image frames.

关 键 词：微弱运动目标 背景杂波抑制 形态学Tophat 小波域平滑滤波:运动目标检测 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]