基于稳健背景子空间的高光谱图像异常检测  

Robust Background Subspace Based Anomaly Detection Algorithm for Hyperspectral Imagery

作　　者：蒲晓丰[1] 雷武虎[1] 黄涛[1] 王迪[1] 

机构地区：[1]电子工程学院脉冲功率激光技术国家重点实验室,合肥230037

出　　处：《光子学报》2010年第12期2224-2228,共5页Acta Photonica Sinica

基　　金：安徽省自然科学基金(070415217)资助

摘　　要：在RX算法中,局部背景协方差矩阵估计会由于背景受到异常像元的"污染"而不能准确反映背景分布,从而导致检测性能下降.针对这一问题,提出一种基于稳健背景子空间的异常检测算法.利用空间秩深度度量背景中每个样本相对于整个背景样本分布空间的位置,将"游离"于背景分布空间之外的样本看作是潜在的异常样本,并将其映射到背景分布空间之内.在此基础上,通过估计背景的协方差矩阵,利用主成分分析构造能更精确地刻画背景的子空间,在该子空间进行了基于马氏距离的检测异常.模拟和真实数据验证了该算法的有效性.In RX anomaly detection algorithm,when backgroud being contaminated from anomaly pixels,the local backgroud covariance matrix estimation can not reflect backgroud distribution accurately,which results in low detection capacity.To overcome this problem,a new method based on the robust background subspace was proposed.Utilizing the spatial rank depth,the position of every sample relative to the distribution space of whole background samples could be measured.Samples which locating at the edge of the distribution space were regarded as anomaly,and being mapped into the distribution space.In this way,the local background covariance matrix was estimated,and the principal component analysis as background space was obtained which can characterize background more accurately.An anomaly detection model was constructed on this subspace using mahalanobis distance.The effectiveness of the proposed method is validated by experimental results from simulated and real data.

关 键 词：异常检测 背景子空间 空间秩深度 映射 高光谱图像 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]