运用归一化光谱混合模型分析城市地表组成

运用归一化光谱混合分析(NSMA)方法,用ETM 数据调查广州市海珠区城市地表组成,采用亮度标准化方法减小亮度变化。通过标准化,使亮度差异在每个植被-非渗透性表面-土壤-水体(V-I-S-W)组成中减小或者消除,这样使得一个单一的端元能够代表一种地表组分。在此基础上,通过归一化影像,选择了植被、非渗透性表面、土壤和水体4种端元,运用一种约束光谱混合分析(SMA)模型,分解了不同种类的城市地表组成。通过与已有模型计算结果比较,认为本文所构建的模型较优,其对研究区非渗透性表面估计的均方根误差为12.6%。

URBAN SURFACE COMPOSITION ANALYSIS BASED ON THE NORMALIZED SPECTRAL MIXTURE MODEL

With rapid urban growth in recent years,the understanding of urban biophysical composition and dynamics has become an important research topic.Remote sensing technologies constitute a potentially scientific basis for examining urban composition and monitoring its changes over the time.The vegetation-impervious surface-soil-water(V-I-S-W) model,in particular,provides a foundation for describing urban/suburban environments and also serves as a basis for further urban analyses comprising urban growth modeling,environmental impact analysis,and socioeconomic factor estimation.This paper developed a normalized spectral mixture analysis(NSMA) method for examining urban composition in Haizhu district using Landsat ETM~ data.In particular,a brightness normalization method was applied to reduce brightness variation.Through this normalization,brightness variability within each V-I-S-W component was reduced or eliminated,thus allowing a single end-member to represent each component.Furthermore,with the normalized image,four end-members,namely vegetation,impervious surface,soil,and water,were chosen to model heterogeneous urban composition using a constrained spectral mixture analysis(SMA) model.The accuracy of impervious surface estimation was assessed and compared with the other existing models.The results indicate that the proposed model is a better alternative to existing models,with a root mean square error(RMSE) of 12.6% for impervious surface estimation in the study area.