基于空时稀疏表示的红外小目标检测算法

doi:10.3969/j.issn.1000-1093.2015.07.016

兵工学报 ›› 2015, Vol. 36 ›› Issue (7): 1273-1279.doi: 10.3969/j.issn.1000-1093.2015.07.016

基于空时稀疏表示的红外小目标检测算法

李正周¹, 侯倩¹, 戴真¹, 付红霞¹, 葛丰增¹,　金钢²

（1.重庆大学通信工程学院，重庆 400044; 2.中国空气动力研究与发展中心，四川绵阳 621000)

收稿日期:2014-11-14 修回日期:2014-11-14 上线日期:2015-09-21
通讯作者: 李正周 E-mail:lizhengzhou@cqu.edu.cn
作者简介:李正周（1974—），男，教授，博士生导师
基金资助:
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)；中央高校基本科研业务费专项资金项目 (106112013CDJZR160007、106112014CDJZR165502)；2013年重庆高校创新团队建设计划项目(KJTD201331)

Dim Moving Target Detection Algorithm Based on Spatial-temporal Sparse Representation

LI Zheng-zhou¹, HOU Qian¹, DAI Zhen¹, FU Hong-xia¹, GE Feng-zeng¹, JIN Gang²

(1.College of Communication Engineering, Chongqing University, Chongqing 400044, China;2.China Aerodynamics Research and Development Center, Mianyang 621000, Sichuan, China)

Received:2014-11-14 Revised:2014-11-14 Online:2015-09-21
Contact: LI Zheng-zhou E-mail:lizhengzhou@cqu.edu.cn

摘要/Abstract

摘要： 提出了一种基于过完备空时字典及其稀疏表示的红外小弱目标运动检测算法。采用K奇异值分解算法学习连续多帧图像的运动信息和形态特征，构建自适应形态过完备空时字典；利用高斯运动模型检验自适应形态过完备空时字典，将其划分为能分别描述目标与背景的目标过完备空时字典和背景过完备空时字典；将连续多帧图像分别在目标过完备空时字典和背景过完备空时字典上稀疏分解，利用几个最大稀疏系数及其空时原子重构信号，增强二者残差来检测小目标信号。实验结果表明，该过完备空时字典不仅能同时描述目标与背景的运动信息和形态特征，极大地提高信号表示的稀疏程度，而且能有效增强目标与背景的特征差异，提高小运动目标的探测能力。提出了一种基于过完备空时字典及其稀疏表示的红外小弱目标运动检测算法。采用K奇异值分解算法学习连续多帧图像的运动信息和形态特征，构建自适应形态过完备空时字典；利用高斯运动模型检验自适应形态过完备空时字典，将其划分为能分别描述目标与背景的目标过完备空时字典和背景过完备空时字典；将连续多帧图像分别在目标过完备空时字典和背景过完备空时字典上稀疏分解，利用几个最大稀疏系数及其空时原子重构信号，增强二者残差来检测小目标信号。实验结果表明，该过完备空时字典不仅能同时描述目标与背景的运动信息和形态特征，极大地提高信号表示的稀疏程度，而且能有效增强目标与背景的特征差异，提高小运动目标的探测能力。

关键词: 信息处理技术, 小弱目标检测, 空时超完备字典, 目标空时字典, 背景空时字典, 信号稀疏重构

Abstract: A dim moving target detection algorithm based on over-complete spatial-temporal dictionary and sparse representation is proposed. A spatial-temporal adaptive morphological over-complete dictionary is trained and constructed according to infrared image sequence. It can represent the motion information and morphological feature of target and background clutter. The spatial-temporal morphological over-complete dictionary is subdivided into two categories: target over-complete spatial-temporal dictionary for describing moving target, and background over-complete spatial-temporal dictionary for embedding background. The criteria adopted to distinguish the target spatial-temporal redundant dictionary from the background spatial-temporal redundant dictionary is that the atom in target over-complete spatial-temporal dictionary could be decomposed more sparsely over Gaussian over-complete spatial-temporal dictionary. Subsequently, the image sequence is decomposed on the target and background over-complete spatial-temporal dictionaries, respectively. The dim moving target and background clutter can be sparsely decomposed on their corresponding over-complete spatial-temporal dictionary, yet it couldn’t be sparsely decomposed on their background over-complete spatial-temporal dictionary. Therefore, the target and background clutter would be reconstructed effectively by prescribed number of atoms with maximum sparse coefficients in their corresponding over-complete spatial-temporal dictionary, and their residuals would differ so visibly to distinguish target from background clutter. The results show that the proposed approach not only could improve the sparsity more efficiently for dim target image sequence, but also could improve the performance of small target detection.

Key words: information processing technology, dim target detection, spatial-temporal redundant dictionary, target spatial-temporal redundant dictionary, background spatial-temporal redundant dictionary, signal sparse reconstruction

李正周, 侯倩, 戴真, 付红霞, 葛丰增,　金钢. 基于空时稀疏表示的红外小目标检测算法[J]. 兵工学报, 2015, 36(7): 1273-1279.

LI Zheng-zhou, HOU Qian, DAI Zhen, FU Hong-xia, GE Feng-zeng, JIN Gang. Dim Moving Target Detection Algorithm Based on Spatial-temporal Sparse Representation[J]. Acta Armamentarii, 2015, 36(7): 1273-1279.

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基于空时稀疏表示的红外小目标检测算法

Dim Moving Target Detection Algorithm Based on Spatial-temporal Sparse Representation

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