Vertex component analysis: A fast algorithm to unmix hyperspectral data
Vertex component analysis: A fast algorithm to unmix hyperspectral data
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DOI：
10.1109/tgrs.2005.844293
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发表时间：
2005-04-01
影响因子：
8.2
通讯作者：
Dias, JMB
中科院分区：
文献类型：
--
作者：
Nascimento, JMP;Dias, JMB
给定一组混合光谱（多光谱或高光谱）向量，线性光谱混合分析或线性解混的目的是估计参考物质（也称为端元）的数量，它们的光谱特征及其丰度分数。本文提出了一种新的方法，从高光谱数据的无监督端元提取，称为顶点分量分析（VCA）。该算法利用了两个事实：1）端元是单形的顶点; 2）单形的仿射变换也是单形。在一系列使用模拟和真实的数据的实验中，VCA算法与最先进的方法竞争，其计算复杂度比最佳可用方法低一到两个数量级。
Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: 1) the endmembers are the vertices of a simplex and 2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.