文献类型：期刊文献

中文题名：结合图像纹理特征的森林郁闭度遥感估测

英文题名：Remote Sensing Estimation of Forest Canopy Density Combined with Texture Features

作者：吴飏[1] 张登荣[1,2] 张汉奎[1] 武红敢[3]

第一作者：吴飏

机构：[1]浙江大学空间信息技术研究所,杭州310027;[2]杭州师范大学遥感与地球科学研究院,杭州310036;[3]中国林业科学研究院资源信息研究所,北京100091

年份：2012

卷号：48

期号：2

起止页码：48-53

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：CSTPCD;;Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2011_2012】；

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)

语种：中文

中文关键词：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

外文关键词：canopy density; texture; gray level co-occurrence matrix（GLCM）; block-oriented; principal component analysis（PCA）; stepwise linear regression

分类号：S771.8

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%.