Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
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DOI：
10.1109/jstars.2012.2194696
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发表时间：
2012-04-01
影响因子：
5.5
通讯作者：
Chanussot, Jocelyn
中科院分区：
文献类型：
--
作者：
Bioucas-Dias, Jose M.;Plaza, Antonio;Chanussot, Jocelyn
成像光谱仪测量在其瞬时视场中散射的电磁能量，具有比多光谱相机更高的光谱分辨率，具有数百或数千个光谱通道。因此，成像光谱仪通常被称为高光谱相机（HSC）。更高的光谱分辨率可以通过光谱分析进行材料识别，这有助于在不适合经典光谱分析的情况下识别材料的无数应用。由于HSC的低空间分辨率、微观材料混合和多重散射，由HSC测量的光谱是场景中材料光谱的混合。因此，准确的估计需要解混。像素被假定为几种材料的混合物，称为端元。解混涉及估计全部或部分：端元的数量，它们的光谱特征，以及它们在每个像素的丰度。由于模型不准确、观测噪声、环境条件、端元变异性和数据集大小等因素，解混是一个具有挑战性的不适定逆问题。研究人员已经设计并研究了许多模型，以寻找鲁棒、稳定、易处理和准确的解混算法。本文概述了从Keshava和Mustard的解混教程[1]到现在的解混方法。首先讨论混合模型。信号子空间，几何，统计，稀疏性为基础的，和空间上下文解混算法进行了说明。描述了数学问题和潜在的解决方案。算法特性的实验说明。
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.