Nonlinear Hyperspectral Unmixing Based on Geometric Characteristics of Bilinear Mixture Models
Nonlinear Hyperspectral Unmixing Based on Geometric Characteristics of Bilinear Mixture Models
复制标题
基于双线性混合模型几何特性的非线性高光谱解混
DOI：
10.1109/tgrs.2017.2753847
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发表时间：
2018-02
影响因子：
8.2
通讯作者：
Zongmin Wu
中科院分区：
文献类型：
--
作者：
Bin Yang;Bin Wang;Zongmin Wu
近年来，人们提出了许多基于双线性混合模型的非线性光谱分解算法。然而，高计算复杂度和真实端元和虚拟端元之间的固有共线性大大降低了这些算法的解混性能。本文提出了一种新的丰度估计算法的基础上的BERRY。基于Bennett的几何特性，我们引入了一个非线性顶点${p}$来代替所有的虚拟端元。与虚拟端元不同，这个顶点${p}$实际上是作为一个附加的真实端元，它给出了像素与其他真实端元的仿射表示。当获得像素相对于真实端元的归一化重心坐标时，将其直接投影为其近似线性混合分量，从而有效地消除了共线性，使得进一步的线性光谱分解成为可能。然后，在分析投影偏差的基础上，分别提出了投影梯度算法和传统线性光谱分解算法两种校正偏差的策略，以提高丰度估计的精度。仿真和真实的高光谱数据的实验结果表明，该算法的性能优于传统的和最先进的光谱分解算法。同时提高了解混的精度和速度。
Recently, many nonlinear spectral unmixing algorithms that use various bilinear mixture models (BMMs) have been proposed. However, the high computational complexity and intrinsic collinearity between true endmembers and virtual endmembers considerably decrease these algorithms’ unmixing performances. In this paper, we come up with a novel abundance estimation algorithm based on the BMMs. Motivated by BMMs’ geometric characteristics that are related to collinearity, we conduct a unique nonlinear vertex ${p}$ to replace all the virtual endmembers. Unlike the virtual endmembers, this vertex ${p}$ actually works as an additional true endmember that gives affine representations of pixels with other true endmembers. When the pixels’ normalized barycentric coordinates with respect to true endmembers are obtained, they will be directly projected to be their approximate linear mixture components, which removes the collinearity effectively and enables further linear spectral unmixing. After that, based on the analysis of projection bias, two strategies using the projected gradient algorithm and a traditional linear spectral unmixing algorithm, respectively, are provided to correct the bias and estimate more accurate abundances. The experimental results on simulated and real hyperspectral data show that the proposed algorithm performs better compared with both traditional and state-of-the-art spectral unmixing algorithms. Both the unmixing accuracy and speed have been improved.
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发表时间：
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影响因子：
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