低秩张量子空间学习红外小目标检测  

Infrared Small Target Detection Based on Low⁃Rank Tensor Subspace Learning

作　　者：王衍[1] 胡宏博 彭真明 WANG Yan;HU Hongbo;PENG Zhenming(Beijing Institute of Space Mechanics&Electricity,Beijing 100094,China;School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]北京空间机电研究所,北京100094 [2]电子科技大学信息与通信工程学院,成都611731

出　　处：《数据采集与处理》2025年第2期349-364,共16页Journal of Data Acquisition and Processing

基　　金：四川省自然科学基金(2025ZNSFSC0522);国家自然科学基金(61571096)。

摘　　要：红外目标检测系统是可靠探测和识别背景辐射与其他干扰条件下高价值目标的有效技术手段之一,广泛应用于各个领域。红外弱小目标检测作为系统的重要组成部分,仍是当前具有挑战性的关键核心技术。本文提出了一种基于低秩张量子空间学习的方法,该方法在考虑序列在空时连续一致性的同时,也保留了红外图像结构的完整性。通过空时滑动窗获得空时张量块模型,利用多子空间学习策略构建不同场景下的红外张量字典模型。最后,采用最优化算法求解所提出的红外张量目标函数,获得低秩背景和稀疏目标张量,通过重构图像检测出感兴趣的红外弱小目标。实验结果表明,在复杂背景高反虚警环境及组合强干扰场景下,该方法目标检测性能优于其他现有检测算法。Infrared target detection system is one of the effective technical means for reliably detecting and identifying high-value targets under the conditions of background radiation and other interferences,and it is widely used in various fields.Infrared weak target detection,as an important part of the system,is still a challenging key core technology at present.In this paper,a method based on low-rank tensor spatial learning is proposed,which preserves the structural integrity of the infrared image while considering the consistency of the sequences in the spatio-temporal continuum.The spatio-temporal tensor block model is obtained through a spatio-temporal sliding window,and the infrared tensor dictionary model is constructed under different scenes using a multi-subspace learning strategy.Finally,an optimization algorithm is used to solve the proposed infrared tensor objective function to obtain the low-rank background and sparse target tensor,and the interested infrared weak targets are detected by reconstructing the image.Experimental results show that the method outperforms other existing detection algorithms for target detection in complex⁃background environments with high⁃reflection⁃induced false alarms and combined strong interference scenarios.

关 键 词：空时结构张量 低秩稀疏逼近 子空间学习 红外小目标检测 组合干扰场景 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]