Two-Step Constrained Nonlinear Spectral Mixture Analysis Method for Mitigating the Collinearity Effect
Two-Step Constrained Nonlinear Spectral Mixture Analysis Method for Mitigating the Collinearity Effect
复制标题
减轻共线性效应的两步约束非线性谱混合分析方法
DOI：
10.1109/tgrs.2015.2506725
复制
发表时间：
2016-05-01
影响因子：
8.2
通讯作者：
Chen, Xuehong
中科院分区：
文献类型：
--
作者：
Ma, Lei;Chen, Jin;Chen, Xuehong
光谱混合分析（SMA）被广泛用于量化混合像元的每个分量（端元）的分数，这些混合像元包含来自多个陆面类型的光谱信号。一般来说，非线性SMA（NSMA）优于线性SMA（LSMA）在植被（树，灌木，作物和草）和土壤混合物的情况下，因为NSMA考虑了显着的多重散射，存在这些混合物。然而，与LSMA相比，双线性NSMA方法（典型的基于物理的NSMA方法）因其对共线性效应的敏感性而受到损害。本文提出了一种两步约束NSMA方法（简称TsC-NSMA）来消除双线性NSMA方法中的共线性效应。理论上的最大似然范围是数学推导出的每个端元分数，和范围被用作额外的约束条件的双线性NSMA方法，以优化解混结果。三个不同的数据集，包括模拟光谱数据，在现场地面图光谱测量，和Landsat 8业务陆地成像仪图像，被用来评估的TsC-NSMA方法的性能。结果表明，TsC-NSMA实现了最高的估计精度为所有混合的情况下，无论是包含严重的端元共线性或高噪声水平，从而表明其能够减轻共线性效应的双线性NSMA方法的潜力，以提高端元分数的估计在实际应用中。
Spectral mixture analysis (SMA) is widely used to quantify the fraction of each component (endmember) of mixed pixels that contain spectral signals from more than one land surface type. Generally, nonlinear SMA (NSMA) outperforms linear SMA (LSMA) in the vegetation (tree, shrub, crop, and grass) and soil mixture case because NSMA considers the significant multiple scattering that exists for these mixtures. However, compared to LSMA, the bilinear NSMA method, which is a typical physical-based NSMA method, is undermined by its susceptibility to the collinearity effect. In this paper, a two-step constrained NSMA method (referred to as TsC-NSMA) is proposed to mitigate the collinearity effect in the bilinear NSMA method. The theoretical maximum likelihood range is mathematically derived for each endmember fraction, and the ranges are used as additional constraints for the bilinear NSMA method to optimize the unmixing results. Three different data sets, including simulated spectral data, an in situ ground plot spectral measurement, and a Landsat8 Operational Land Imager image, were used to assess the performance of the TsC-NSMA method. The results indicated that TsC-NSMA achieved the highest estimation accuracy for all mixed scenarios which either contain severe endmember collinearity or high noise levels, thereby suggesting its ability to mitigate the collinearity effect in the bilinear NSMA method with the potential to improve the estimation of endmember fractions in practical applications.