基于谱域-空域结合的高光谱异常探测  

Spectral-spatial Joint Method for Hyper-spectral Anomaly Detection

作　　者：雷武虎[1,2,3] 任晓东[1,2,3] 孙越娇 王迪[1,2,3] 

机构地区：[1]脉冲功率激光技术国家重点实验室,合肥230037 [2]安徽省电子制约技术重点实验室,合肥230037 [3]电子工程学院,合肥230037

出　　处：《光电技术应用》2016年第5期36-41,共6页Electro-Optic Technology Application

摘　　要：针对高光谱遥感空域信息利用率低给侦查带来的问题,提出了一种基于谱域-空域联合特征的异常检测算法。首先,利用光谱梯度角余弦值给邻域像元赋予权值,通过对邻域像元加权求和的方式得到空域特征;将空域特征与谱域特征进行加权拟合得到谱域-空域联合特征;然后,将由谱域-空域联合特征所组成的高光谱影像进行主成分分析以提取主要成分进行异常检测。通过对比异常检测效果二值图和ROC曲线,说明算法具有优越性,能够提高检测效果。For the reconnaissance problem of the spatial information＇ s low utilizatio in hyper-spectral sensing, a novel anomaly detection algorithm based on spectral domain-spatial joint characteristics is proposed. At first, each neighborhood pixel is given weights through the cosine of the spectral gradient angle and the spatial feature is gotten by adding the weighted neighborhood pixels together. The spectral domain-spatial joint characteristic is gotten by adding the weighted spectral feature and the spatial feature together. And then, the hyper-spectral data with the spectral domain-spatial joint characteristics is carried out principal component analysis to extract the main components to carry on anomaly detection. At end, from the binary image and the receiver operating characteristic curve （ROC） of the anomaly detection, it can be seen that the proposed algorithm has superiority and it could improve the detection effect.

关 键 词：高光谱 空谱结合 主成分分析 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]