高光谱图像自适应核联合表示异常检测  

Adaptive kernel collaborative representation anomaly detection for hyperspectral imagery

作　　者：唐意东[1] 黄树彩[1] 凌强[1] 钟宇[1] 

机构地区：[1]空军工程大学大学防空反导学院,西安710051

出　　处：《强激光与粒子束》2015年第9期49-55,共7页High Power Laser and Particle Beams

基　　金：航空科学基金资助课题(20130196004)

摘　　要：针对高光谱图像目标检测问题,提出了一种基于自适应核联合表示的高光谱图像异常检测方法。相比于稀疏表示强调系数向量的稀疏性,基于联合表示的检测算法更强调背景字典中每一原子对联合表示的贡献,并且系数求解更加简单直接;在基于向量2-范数极小化问题求解联合表示系数时,引入相似性正则化矩阵与和为1的条件对系数进行约束,以增强算法的稳定性和分辨力;进而将联合表示检测算法扩展到核空间,利用高光谱图像的局部统计特性进行核参数自适应选取,增强了核参数的局部适应性。为验证该方法的有效性,采用一幅实测高光谱图像中的两个感兴趣区域进行仿真实验,并与传统异常检测方法的检测结果进行对比分析,结果表明该方法具有较传统异常检测方法更出色的检测效果。An adaptive kernel collaborative representation based anomaly detector for hyperspectral imagery was proposed in view of target detection.Although the sparse representation considers the sparsity of weighted vector,the collaborative representation puts more emphasis on each atom's contribution to the linear combination.A comparability weighted regularization matrix and sum-to-one constraint were employed to reinforce the stability and separability.Then,the kernel parameter was adaptively estimated by using local statistics to improve its own local adaption.The proposed AKCRD algorithm was applied to two hyperspectral datasets comparing with other algorithms such as RX,KRX,SVDD,CRD and KCRD.The simulation results show that the proposed algorithm has better detection performance.

关 键 词：高光谱图像 联合表示 自适应核联合表示 异常检测 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]