基于光谱空间重构的非监督最邻近规则子空间的高光谱异常检测  

Unsupervised Nearest Regularized Subspace Based on Spectral Space Reconstruction for Hyperspectral Anomaly Detection

作　　者：王志威 谭琨[1,2] 王雪 丁建伟[3] 陈宇[1] WANG Zhi-wei;TAN Kun;WANG Xue;DING Jian-wei;CHEN Yu(Key Laboratory of Land,Environment and Disaster Monitoring,Ministry of Natural Resources,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China;Key Laboratory of Geographic Information Science,Ministry of Education,East China Normal University,Shanghai 200241,China;The Second Surveying and Mapping Institute of Hebei,Shijiazhuang,050037,China)

机构地区：[1]中国矿业大学自然资源部国土环境与灾害监测重点实验室,江苏徐州221116 [2]华东师范大学地理信息科学教育部重点实验室,上海200241 [3]河北省第二测绘院,石家庄050037

出　　处：《光子学报》2020年第6期74-85,共12页Acta Photonica Sinica

基　　金：国家自然科学基金(Nos.41871337,41471356)。

摘　　要：针对高光谱遥感影像维数高、数据量巨大且地物分布复杂,导致背景与异常难以区分的问题,提出一种基于光谱空间重构的非监督最邻近规则子空间异常探测算法.首先通过基于结构张量的波段选择算法,去除噪声像元,选择更有效的波段.然后,通过光谱空间重构增加背景与异常的绝对光谱距离.最后,为了充分利用背景字典之间的空间相似性信息,将空间距离权重引入到非监督最邻近规则子空间算法中,提高检测精度.为验证所提算法的有效性,用四组真实的高光谱数据进行实验,研究了不同参数对检测结果的影响.结果表明,与其他异常检测算法对比,所提算法具有更好的检测效果.The high dimension and huge data volume of hyperspectral remote sensing images and the complexity of surface feature lead to difficulty in distinguishing the anomaly pixel from the background.To solve these problems,an unsupervised nearest regularized subspace anomaly detection algorithm based on spectral space reconstruction is proposed.Firstly,in the process of band selection based on structure tensor,noise pixels are removed to obtain more effective bands.Then,the spectral space reconstruction is utilized to increase the absolute spectral distance between the background and the anomaly.Finally,to take full advantage of the spatial similarity information between background dictionaries,the spatial distance weight is introduced into the unsupervised nearest regularized subspace algorithm to improve the accuracy of linear representation.To validate the effectiveness of the proposed algorithm,experiments on four sets of real hyperspectral data are conducted,and the infulence of different parameters on the detection results is studied.Experimental results demonstrate that the proposed algorithm has a better detective performance than other anomaly detection algorithms.

关 键 词：高光谱影像 异常探测 波段选择 光谱空间重构 非监督最邻近规则子空间 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]