1) vegetation remote sensing
植被光谱遥感
1.
Review of the application of vegetation remote sensing;
植被光谱遥感数据的研究现状及其展望
2) vegetation classification using hyperspectral remote sensing
植被高光谱遥感分类
1.
Then,taking an OMISⅠimage as an example, performs many tests to present and analyze the laws of this model applied in vegetation classification using hyperspectral remote sensing.
遥感图像中普遍存在着混合像元 ,这部分像元的分解一直是遥感应用研究的热点和难点 ,该文简要介绍混合像元的概念、研究现状和广泛用于混合像元分解的线性混合光谱模型及其解算方法 ,然后以OMISⅠ高光谱遥感数据为例 ,通过大量实验对该模型在植被高光谱遥感分类中的应用做了详细地探讨和分析。
2.
This paper focuses on the establishment of a nonlinear mixing spectral model and its application to the vegetation classification using hyperspectral remote sensing, and discusses the application of vegetation classification methods of hyperspectral images, the selection of training samples and unmixing of mixed pixels.
本文围绕非线性混合光谱模型的建立及其在植被高光谱遥感分类中的应用研究这个中心，论述植被高光谱遥感分类方法的应用、训练样本的选择以及混合像元的分解。
3) vegetation remote sensing
植被遥感
1.
Application of five atmospheric correction models for Landsat TM data in vegetation remote sensing.;
五种TM影像大气校正模型在植被遥感中的应用
4) vegetation spectrum
植被光谱
1.
The relations of grassland bio-parameters related with grassland health were analyzed by Principal Component Analysis(PCA) on the basis of combining community survey and vegetation spectrum in Xilin River basin,Inner Mongolia.
(2)从6波段的植被光谱反射数据中提取出2个主成分:可见光因子和红外光因子,它们可以较好反映植被信息。
5) vegetation remote sensing anomaly
植被遥感异常
1.
The spectral reflectivities of the leaves of the abnormal vegetation could be higher or lower than that of the normal vegetation,which showed different gray levels between the normal and abnormal vegetation on remote sensing images——the vegetation remote sensing anomaly.
植物对金有吸收和积聚作用,金矿区上的植被由于吸收过量的金及伴生元素而表现出生态上的异常;异常植被的波谱反射率与正常植被相比,或高或低,差异是存在的;反映在遥感图象上,表现为异常植被和正常植被灰度值的差异,即植被遥感异常;因此,在植被覆盖区可以通过与金矿化有关的异常植被信息提取获得找矿信息。
6) Quantitative remote senisng of vegetation
植被定量遥感
补充资料：高光谱分辨率遥感图像及图像光谱信息提取
高光谱分辨率遥感图像及图像光谱信息提取
高光谱分辨率遥感图像及图像光谱信息提取 郑兰芬供稿
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条