Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping
Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping
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用于不透水表面测绘的中等分辨率图像的分层光谱混合分析
DOI：
10.1016/j.jag.2017.04.006
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发表时间：
2017-04
影响因子：
7.5
通讯作者：
Jia Xiuping
中科院分区：
文献类型：
--
作者：
Sun Genyun;Chen Xiaolin;Ren Jinchang;Zhang Aizhu;Jia Xiuping
线性混合谱分析(LSMA)被广泛应用于不透水表面的估计，特别是在中空间分辨率图像中不透水表面丰度的估计。然而，由于类内光谱的可变性，以及不同像素之间端元类别的数量和类型的不同，导致不透水表面的高估或低估，这给端元的选择带来了困难。分层被认为是解决这一问题的一个有前途的过程。提出了一种基于光谱域的分层混合光谱分析方法(Sp_SSMA)。它根据组合重建指数(CBI)、颜色空间中的强度分量和归一化差异植被指数(NDVI)值将整个数据分为三组。为每个基团开发了一个合适的端元模型，以适应基团之间的光谱变化。分解成每个组中端元的关联子集(或全集)可以使分解适应于每个像素实际包含的端元类的类型。结果表明，Sp_SSMA方法在R、RMSE和SE方面均优于全集端元SMA和基于先验知识的混合谱分析(PKSMA)。
Linear spectral mixture analysis (LSMA) is widely employed in impervious surface estimation, especially for estimating impervious surface abundance in medium spatial resolution images. However, it suffers from a difficulty in endmember selection due to within-class spectral variability and the variation in the number and the type of endmember classes contained from pixel to pixel, which may lead to over or under estimation of impervious surface. Stratification is considered as a promising process to address the problem. This paper presents a stratified spectral mixture analysis in spectral domain (Sp_SSMA) for impervious surface mapping. It categorizes the entire data into three groups based on the Combinational Build-up Index (CBI), the intensity component in the color space and the Normalized Difference Vegetation Index (NDVI) values. A suitable endmember model is developed for each group to accommodate the spectral variation from group to group. The unmixing into the associated subset (or full set) of endmembers in each group can make the unmixing adaptive to the types of endmember classes that each pixel actually contains. Results indicate that the Sp_SSMA method achieves a better performance than full-set-endmember SMA and prior-knowledge-based spectral mixture analysis (PKSMA) in terms of R, RMSE and SE.
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DOI：
--
发表时间：
2007
期刊：
Journal of Northeastern University
影响因子：
--
作者：
Zhao Jin-peng
通讯作者：
Zhao Jin-peng
影响因子：
3.4
作者：
Qihao Weng;Xuefei Hu;D. Lu
通讯作者：
Qihao Weng;Xuefei Hu;D. Lu
影响因子：
13.5
作者：
Kato, Soushi;Yamaguchi, Yasushi
通讯作者：
Yamaguchi, Yasushi
利用双极化 SAR 数据绘制城市不透水表面：一种改进方法
影响因子：
9.1
作者：
Zhang Hongsheng;Lin Hui;Li Yu;Zhang Yuanzhi;Fang Chaoyang
通讯作者：
Fang Chaoyang
DOI：
10.1109/jstars.2015.2464698
发表时间：
2016-03-01
影响因子：
5.5
作者：
Baghdadi, Nicolas N.;El Hajj, Mohamad;Fayad, Ibrahim
通讯作者：
Fayad, Ibrahim