摘要
混合像元分解技术(Spectral Mixture Analysis,SMA)是遥感图像处理的重要手段之一.传统方法假设每个端元具有稳定的光谱特征,然而端元内光谱差异普遍存在,这将导致混合像元分解精度的降低.针对该问题,提出了基于Fisher判别(Fisher Discriminant Analysis,FDA)的混合像元分解算法.Fisher判别对光谱各波段进行线性组合,使得转换后的光谱值分离度最大,即端元内的光谱差异较小而端元间的光谱差异较大.利用转换后的光谱对混合像元进行分解可以最大程度地减少端元内光谱差异对分解结果的影响.利用该方法对室内控制实验的模拟混合像元光谱进行分解,并与过去提出的几种混合像元分解技术进行比较,结果显示新方法相比传统方法在分解精度上有相当程度的提高.
Spectral mixture analysis （SMA） is one of the most important methods in remote sensing image processing. Traditional SMA assumes a constant spectral signature for each endmember. However, the endmember spectral variability commonly exists, which leads to the lower accuracy of pixel unmixing. In order to solve this problem, a novel unmixing method based on Fisher discriminant analysis （FDA） was developed. FDA aimed to find a linear combination of the spectral bands for getting the largest separation degree among the endmember spectra, i.e. small variability of spectra inside one endmember group but a large difference of spectra among endmember groups. Mixture pixel was unmixed by using transformed spectra, as a result, the adverse impact caused by endmember spectral variability on unmixng accuracy could be diminished to a large extent. A laboratory experiment was designed to obtain a group of mixed spectra with endmember spectral variability. The measured spectra were used to test the performance of the new method and the traditional SMA methods. The comparison results suggest that the new method outperforms the traditional methods with considerable improvement of unmixing accuracy.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2009年第6期476-480,共5页
Journal of Infrared and Millimeter Waves
基金
国家高技术研究发展计划(863计划)(2006AA12Z103)
教育部新世纪人才计划资助
关键词
混合像元分解
端元内光谱差异
FISHER判别
室内控制实验
spectral mixture analysis
endmember spectral variability
Fisher discriminant analysis
laboratory experiment
作者简介
陈学泓（1985-），男，福建泉州人，硕士研究生，现从事资源环境遥感应用研究．
通讯作者：陈晋（1967-），博士，教授，Email：chenjin@ires．cn
