粗定位和协同表示的高光谱图像异常检测  

Rough location and collaborative representation for hyperspectral image anomaly detection

作　　者：胡静 赵明华[1] 李鹏[1] 李云松[2] Hu Jing;Zhao Minghua;Li Peng;Li Yunsong(School of Computer Science and Engineering,Xi′an University of Technology,Xi′an 710048,China;State Key Laboratory of Integrated Services Networks,Xidian University,Xi′an 710071,China)

机构地区：[1]西安理工大学计算机科学与工程学院,西安710048 [2]西安电子科技大学综合业务网及关键技术国家重点实验室,西安710071

出　　处：《中国图象图形学报》2021年第8期1871-1885,共15页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61901362);教育部春晖计划项目(112-425920021);陕西省自然科学基金项目(2019JQ-729);西安理工大学校博士启动项目(112/256081809)。

摘　　要：目的由于在军事和民用应用中的重要作用,高光谱遥感影像异常检测在过去的20~30年里一直都是备受关注的研究热点。然而,考虑到异常点往往藏匿于大量的背景像元之中,且只占据很少的数量,给精确检测带来了不小的挑战。针对此问题,基于异常点往往表现在高频的细节区域这一前提,本文提出了一种基于异常点粗定位和协同表示的高光谱遥感影像异常检测算法。方法对输入的原始高光谱遥感影像进行空间维的降质操作;通过衡量降质后影像与原始影像在空间维的差异,粗略定位可能的异常点位置;将粗定位的异常点位置用于指导像元间的协同表示以重构像元;通过衡量重构像元与原始像元的差异,从而进一步优化异常检测结果。结果在4个数据集上与6种方法进行了实验对比。对于San Diego数据集,次优算法和本文算法分别取得的AUC(area under curve)值为0.9786和0.9940;对于HYDICE(hyperspectral digital image collection equipment)数据集,次优算法和本文算法的AUC值为0.9936和0.9985;对于Honghu数据集,次优算法和本文方法的AUC值分别为0.9992和0.9993;对Grand Isle数据集而言,尽管本文方法以0.001的差距略低于性能第1的算法,但从目视结果图中可见,本文方法所产生的虚警目标远少于性能第1的算法。结论本文所提出的粗定位和协同表示的高光谱异常检测算法,综合考虑了高光谱遥感影像的谱间特性,同时还利用了其空间特性以及空间信息的先验分布,从而获得异常检测结果的提升。Objective Hyperspectral image has rich spectral information.Different materials correspond to different spectral information,which can be applied to disaster warning,agriculture precision,and authenticity identification for some valuable art works.Anomaly detection of hyperspectral images refers to detecting the anomalous pixels in the scene without any prior information,and it is important in military and civil applications.In this way,the anomaly detection of hyperspectral images has gained increasing popularity.The anomalies usually refer to the outliers with spatial and spectral signatures that are severely different from their surroundings.Compared with the background,the anomalies have two main characteristics.First,their spectral information is severely different from that of their surroundings,and this phenomenon is named the spectral difference.Meanwhile,the anomalies are usually embedded into the local homogeneous background in a format of several pixels or even sub-pixels,and this phenomenon is called the spatial difference.Anomalies are often hidden in a large number of background pixels,and they only occupy a small number.Thus,they bring a great challenge to accurate detection.This study proposes a hyperspectral anomaly detection algorithm based on rough localization and collaborative representation of outliers to solve this problem.It is based on the institution that the anomalies often appear in high-frequency detail areas.Method A novel hyperspectral anomaly detection method based on the rough location and collaborative representation is proposed in this study.This method utilizes the spatial information and inter-spectral information carried by the hyperspectral images simultaneously,which ensures the accuracy of the algorithm.Three modules are included in the whole detection process.First,the original hyperspectral image is degraded in spatial dimension.Second,we can obtain the rough response map of spatial anomaly by measuring the difference between the degraded and original images in spatial d

关 键 词：高光谱 遥感影像 异常检测 粗定位 协同表示 

分 类 号：TP751.[自动化与计算机技术—检测技术与自动化装置]