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首页> 外文期刊>BioTechnology: An Indian Journal >Spectral unmixing based on nonnegative matrix factorization restrained by minimizing the sum of the maximum distances between endmembers
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Spectral unmixing based on nonnegative matrix factorization restrained by minimizing the sum of the maximum distances between endmembers

机译：通过最小化末端成员之间最大距离之和来抑制基于非负矩阵分解的频谱分解

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摘要

The spectral signature of each hyperspectral image pixel commonly comprises the combined measured reflectance of components. These pixels are called mixed pixel. Spectral unmixing provides an efficient mechanism for the interpretation and classification of these mixed pixels. The algorithm of minimum volume constrained nonnegative matrix factorization (MVC-NMF) is a kind of algorithm which can extract endmembers from highly mixed hyperspectral images. It does not need to assume that pure pixels exist in hyperspectral images. Endmembers and its abundance can be obtained synchronously by this algorithm. However, the constraint condition of MVC-NMF algorithm need to calculate the volume of simplex, which cause the iterative process is complex and the amount of calculation is very large. This paper proposes a NMF algorithm under new constraint condition, which is restrained by minimizing the sum of all maximum distances between endmembers instead of minimizing simplex volume. Experimental results of spectral unmixing illustrate this new NMF algorithm has higher decomposition accuracy and efficiency than MVC-NMF algorithm.
机译：每个高光谱图像像素的光谱特征通常包括成分的组合测量反射率。这些像素称为混合像素。光谱解混为这些混合像素的解释和分类提供了一种有效的机制。最小体积约束非负矩阵分解算法（MVC-NMF）是一种可以从高度混合的高光谱图像中提取端元的算法。不需要假设高光谱图像中存在纯像素。通过该算法可以同步获得端成员及其丰度。然而，MVC-NMF算法的约束条件需要计算单纯形的数量，这会导致迭代过程复杂且计算量很大。本文提出了一种在新的约束条件下的NMF算法，该算法通过最小化端构件之间所有最大距离的总和而不是最小化单纯形体积来进行约束。频谱分解的实验结果表明，这种新的NMF算法比MVC-NMF算法具有更高的分解精度和效率。

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