摘要
高光谱(hyperspectral)遥感是20世纪末地球观测系统中最重要的技术突破之一。根据植被高光谱数据的植被冠层光谱反射特征和诊断性光谱吸收特征,利用光谱连续统去除法,探讨光谱一阶微分反射比(FDR)和从连续统去除的光谱吸收特征中获得的波段深度(BD)、连续统去除后微分反射比(CRDR)、波段深度比(BDR)和归一化波段深度指数(NBDI)等光谱特征参量。结合多时相的条锈病小麦PHI航空高光谱影像,分析条锈病对小麦光谱的影响及其光谱特征,并运用光谱特征参量和波谱角制图(SAM)技术监测和识别小麦条锈病。
One of the most important technologies in EOS is hyperspectral remote sensing in 1990s. This paper describes vegetation hyperspectral First Derivative Reflectance（FDR） and some feature variables derived from continuum- removed absorption features such as Band Depth（BD）,Continuum- Removed Derivative Reflectance（CRDR）, Band Depth Ratio（BDR）, Normalized Band Depth Index（NBDI） according to thespectral reflected features, diagnostic absorption features of vegetation and Continuum Removal based on the hyperspectral imaging data. By analyzing on the multi- temporal PHI（Pushbroom Hyperspectral Imager） airborne wheat image data acquired from tx）oting stage to milking stage which was infected by stripe rest disease,the influence on crop spectra and spectral features of stripe rest disease were understood and found out. And the disease information was recognized and monitored in terms of the wheat spectral features and SAM（Spectral Angle Mapping） technique.
出处
《地理与地理信息科学》
CSCD
北大核心
2006年第4期31-34,共4页
Geography and Geo-Information Science
关键词
植被光谱特
PHI影像
小麦条锈病
波谱角制图
信息提取
vegetation spectral features
PHI image
stripe rest disease of wheat
spectral angle mapping
information extraction
