摘要
为了解决高光谱目标检测中高斯径向基核、光谱相似度量核难以同时描述光谱曲线整体及局部特性的问题,利用光谱信息散度与梯度角正切相结合的光谱区分方法构造了一种新的核函数.对真实机载可见红外成像光谱仪高光谱数据进行高光谱核异常检测,得到二值图及接收机操作特性曲线.结果表明,在低虚警率下,相比于高斯径向基核、光谱相似度量核,本文所提出核函数在高光谱核异常检测中准确度与清晰度更高.
To solve the problem that the overall and local characteristics of the curves of spectrum are difficultly described by the Gaussian radial basis function and the spectral similarity measurement kernel simultaneously in a hyperspectral imagery detection,a novel kernel anomaly detection method in hyperspectral imagery based on the spectral discrimination method was proposed.A kernel anomaly detection in hyperspectral was conducted by real hyperspectral images collected by airborne visible infrared imaging spectrometer.The binary graph and receiver operating characteristic curve of the anomaly detection were attained.The results show that,for a lower false alarm rate in a hyperspectral imagery detection,compared with the Gaussian radial basis function and the spectral similarity measurement kernel,the proposed kernel can detect the abnormal targets with a higher accuracy and clarity.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2016年第3期138-142,共5页
Acta Photonica Sinica
基金
国家自然科学基金(No.61271353)
安徽省自然科学基金(No.KY11070)资助~~
关键词
高光谱图像
异常检测
光谱区分方法
核函数
RX
Hyperspectral imagery
Anomaly detection
Spectral discrimination method
Kernel function
RX
