基于双倒高斯模型的高光谱数据植被光谱诊断性特征分析及水分反演

植被含水量是影响和评价植被生长状态的重要因素之一。因此,针对高光谱数据具有目标诊断性特征精细反演的特点,较为精准地提取了植被的光谱诊断性特征,在包络线去除法的基础上,提出了基于双倒高斯模型的光谱吸收峰特征参数提取方法。首先,根据植被光谱吸收峰特征建立了双倒高斯模型,其次,为了验证模型的正确性和有效性,利用地面试验数据及真实的Hyperion高光谱遥感数据对模型进行了验证。结果表明:通过模型提取的光谱特征参数:吸收峰深度、对称度与植被含水量呈线性相关,决定系数R2分别为0.86和0.76,RMSE为0.797和1.112。实验结果在证实了模型有效性的同时验证了高光谱数据对于植被含水量反演的可行性。

Estimation of Vegetation Water Content based on Bi|inverted Gaussian Fitting Spectral Feature Analysis Using Hyperspectral Data

Vegetation water content is one of the important factors that influence and evaluate of vegetation growth.This paper aims to the property of hyperspectral data,the spectrum characteristics of vegetation were accurately extracted.Based on the continuum removal,a novel model called bi-inverted Gaussian model for the extraction of absorption characters was explored.Firstly,according to the vegetation spectral absorption characteristics,bi-inverted Gaussian model was founded.Then,in order to verify the validity of this approach,the ground data and the Hyperion hyperspectral remote sensing data was used.The results showed that the depth and the symmetry had the linear correlation with vegetation water content,and the coefficient of determination reached to 0.86 and 0.76,RMSE reached to 0.797 and 1.112.The model was verified and this paper proved the feasibility for classification of vegetation by using hyperspectral data.

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