摘要
在光谱等传统特征的基础上,结合遥感图像的纹理特征估测郁闭度:首先基于面向地块的方法计算图像的灰度共生矩阵纹理特征,然后用主成分方法分析相关性并降维,最后将图像纹理特征和光谱地形等特征一起作为自变量引入到郁闭度估测的逐步回归模型中。结果表明:结合图像纹理特征的方法比传统的只基于光谱或地形特征的方法在估测精度上有较大提高,判别系数R珔2从0.737提高到0.805,估测精度从81.03%提高到84.32%。
The development of high-resolution remote sensing imaging technology provides a new way to the large-scale estimation of forest canopy density.The traditional inversion methods for canopy density only use spectral or topographical features of remote sensing images.However,due to the existence of the different thing with same spectrum and the same thing with different spectrum phenomena,it is difficult to improve the estimation accuracy of canopy density.Based on spectrum and other traditional features,this paper combines texture features of remote sensing images to estimate canopy density.Firstly,the gray level co-occurrence matrix（GLCM） texture features are computed using object-based method.Then,prinicipal component analysis（PCA） method is applied in correlation analysis and dimension reduction of texture features.Finally,spectrum and topographical features together with texture features are introduced into stepwise regression model to estimate canopy density.The experimental results showed that compared with the traditional method only based on spectrum or topographical features,the method combined with texture features greatly improved the estimation accuracy.The coefficient of determination（adjusted 2） increased from 0.737 to 0.805.The estimation accuracy increased from 81.03% to 84.32%.
出处
《林业科学》
EI
CAS
CSCD
北大核心
2012年第2期48-53,共6页
Scientia Silvae Sinicae
基金
国家863课题"面向地块的地物类型精细识别技术及其应用"(2007AA12Z181)
关键词
郁闭度
纹理
灰度共生矩阵
面向地块
主成分分析
逐步线性回归
canopy density
texture
gray level co-occurrence matrix（GLCM）
block-oriented
principal component analysis（PCA）
stepwise linear regression
