基于空时联合稀疏重构的红外小弱运动目标检测方法  

Infrared small moving target detection algorithm based on joint spatio-temporal sparse recovery

作　　者：李正周[1] 侯倩[1] 付红霞[1] 李家宁[1] 杨丽娇[1] 邵万兴 程蓓[1] 

机构地区：[1]重庆大学通信工程学院,重庆400044

出　　处：《强激光与粒子束》2015年第9期26-31,共6页High Power Laser and Particle Beams

基　　金：国家自然科学基金项目(61071191);中国科学院光束控制重点实验室基金项目(2014LBC005);中国博士后基金项目(2014M550455);重庆博士后科研项目特别基金项目(XM201489);2013年重庆高校创新团队建设计划资助项目(KJTD201331);中央高校基本业务费项目(106112013CDJZR160007;106112014CDJZR165502)

摘　　要：提出了一种基于空时联合稀疏重构的红外小弱运动目标检测算法。通过学习序列图像内容而构建的空时联合字典能同时刻画目标或背景的形态特征和运动信息;利用多元高斯运动模式从空时联合字典中提取出目标空时字典和背景空时字典,目标空时过完备字典描述移动的目标,背景空时过完备字典表征背景噪声。将连续多帧图像在空时联合字典上进行稀疏分解,然后分别利用目标空时字典和背景空时字典中的最大稀疏系数及其空时原子重构信号,获取重构残余能量差异来区分目标和背景。试验结果表明,由同源的空时字典重构的残余能量小,而由异构的空时字典恢复的残余能量大,该方法不仅能提高序列信号表示的稀疏度,还能有效提高小运动目标的探测能力。A dim small moving target detection algorithm based on joint spatio-temporal sparse recovery is proposed in this paper.A spatio-temporal over-complete dictionary is firstly trained from infrared image sequence,and it can characterize not only motion information but also morphological feature.In the spatio-temporal over-complete dictionary,the spatio-temporal atom are then classified as target spatio-temporal atoms building target spatio-temporal over-complete dictionary,which describes moving target,and background spatio-temporal atoms constructing background spatio-temporal over-complete dictionary,which embeds background clutter.Infrared image sequence is decomposed on the union of target spatio-temporal over-complete dictionary and background spatio-temporal over-complete dictionary.The residuals after decomposing and reconstruction by the target spatiotemporal over-complete dictionary and background over-complete dictionary differ very distinctly,and they are then adopted to decide the signal is from target or background.Some experiments are conducted and the experimental results show that the residual reconstructed by its homologous spatio-temporal over-complete dictionary is very little,yet the residual recovered by its heterogonous spatio-temporal over-complete dictionary is quite large.This proposed approach could not only improve the sparsity more efficiently,but also enhance the target detection performance more effectively.

关 键 词：小弱目标检测 空时联合字典 信号稀疏复原 信号残差 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]