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Bilinear normal mixing model for spectral unmixing

机译：用于频谱分解的双线性正混合模型

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摘要

Spectral unmixing (SU) is a useful tool for hyperspectral remote sensing image analysis. However, due to the interference of spectral variance and non-linearity caused by photon multiple-scattering, the result might be an inaccuracy. In addition, the unmixing performance of typically relies on the prior knowledge of endmembers. Although many classical endmember extraction algorithms have been presented, it is hard to obtain accurate endmembers in practical applications. This study presents a bilinear normal mixing model named as BNMM to tackle these issues. In fact, BNMM employs the polynomial post-non-linear mixing model to alleviate the effect of non-linearity and uses a normal distribution model to reduce the influence of endmembers variability. Based on the BNMM, the authors develop a Hamiltonian Monte Carlo algorithm for SU. The experimental results demonstrate that the proposed algorithm outperforms other classical unmixing algorithms in the case of simulated and benchmark datasets.
机译：光谱分解（SU）是用于高光谱遥感影像分析的有用工具。但是，由于光子多重散射引起的光谱方差和非线性干扰，结果可能不准确。另外，解混性能通常取决于端构件的先验知识。尽管已经提出了许多经典的端成员提取算法，但是在实际应用中很难获得准确的端成员。这项研究提出了一个名为BNMM的双线性正态混合模型来解决这些问题。实际上，BNMM使用多项式后非线性混合模型来减轻非线性的影响，并使用正态分布模型来减少端成员变异性的影响。基于BNMM，作者开发了用于SU的哈密顿蒙特卡罗算法。实验结果表明，在模拟和基准数据集的情况下，该算法优于其他经典的混合算法。

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