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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Fully Constrained Linear Spectral Unmixing: Analytic Solution Using Fuzzy Sets
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Fully Constrained Linear Spectral Unmixing: Analytic Solution Using Fuzzy Sets

机译：完全约束线性光谱分解：使用模糊集的解析解

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摘要

The linear mixture model is a convenient way to describe image pixels as a linear combination of pure spectra – termed endmembers. The fractional contribution from each endmember is calculated through inversion of the linear model. Despite the simplicity of the model, a nonnegativity constraint that is imposed on the fractions leads to an unmixing problem for which it is hard to find a closed analytical solution. Current solutions to this problem involve iterative algorithms, which are computationally intensive and not appropriate for unmixing large number of pixels. This paper presents an algorithm to build fuzzy membership functions that are equivalent to the least square solution of the fully constrained linear spectral unmixing problem. The efficiency and effectiveness of the proposed solution is demonstrated using both simulated and real data.
机译：线性混合模型是将图像像素描述为纯光谱的线性组合（称为端成员）的便捷方法。通过线性模型的反演来计算每个端部成员的分数贡献。尽管模型简单，但对馏分施加的非负约束会导致分解问题，因此很难找到封闭的解析解。该问题的当前解决方案涉及迭代算法，该算法计算量大并且不适合于分解大量像素。本文提出了一种算法，用于建立模糊隶属函数，该函数与完全约束线性频谱分解问题的最小二乘解等效。使用仿真数据和实际数据演示了所提出解决方案的效率和有效性。

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