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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Two-Step Constrained Nonlinear Spectral Mixture Analysis Method for Mitigating the Collinearity Effect
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Two-Step Constrained Nonlinear Spectral Mixture Analysis Method for Mitigating the Collinearity Effect

机译：减轻共线性效应的两步约束非线性光谱混合分析方法

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摘要

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 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.
机译：光谱混合分析（SMA）被广泛用于量化混合像素的每个成分（末端成员）的比例，这些像素包含来自一种以上陆面类型的光谱信号。通常，在植被（树木，灌木，农作物和草丛）和土壤混合物的情况下，非线性SMA（NSMA）优于线性SMA（LSMA），因为NSMA认为这些混合物存在明显的多重散射。但是，与LSMA相比，双线性NSMA方法是一种典型的基于物理的NSMA方法，但由于其对共线性效应的敏感性而受到破坏。为了缓解双线性NSMA方法中的共线性效应，本文提出了一种两步约束NSMA方法（称为TsC-NSMA）。从数学上推导每个端部成员分数的理论最大似然范围，并将该范围用作双线性NSMA方法的附加约束，以优化解混结果。使用三个不同的数据集（包括模拟光谱数据，地谱光谱测量结果和Landsat8操作性陆地成像仪图像）来评估TsC-NSMA方法的性能。结果表明，TsC-NSMA在包含严重端成员共线性或高噪声水平的所有混合场景中均达到了最高的估计精度，从而表明其在双线性NSMA方法中减轻共线性效应的能力具有改善端成员估计的潜力实际应用中的分数。

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