An approach is proposed and illustrated for the joint selection of essential samples and essential variables of a data matrix in the frame of spectral unmixing. These essential features carry the signals required to linearly recover all the information available in the rows and columns of a data set. Working with hyperspectral images, this approach translates into the selection of essential spectral pixels (ESPs) and essential spatial variables (ESVs). This results in a highly-reduced data set, the benefits of which can be minimized computational effort, meticulous data mining, easier model building as well as better problem understanding or interpretation. Working with both simulated and real data, we show that (i) reduction rates of over 99% can be typically obtained, (ii) multivariate curve resolution - alternating least squares (MCR-ALS) can be easily applied on the reduced data sets and (iii) the full distribution maps and spectral profiles can be readily obtained from the reduced profiles and the reduced data sets (without using the full data matrix).

中文翻译：

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高光谱图像中基本像素和基本变量的联合选择

提出并说明了一种在光谱解混框架下联合选择数据矩阵的基本样本和基本变量的方法。这些基本特征携带线性恢复数据集行和列中所有可用信息所需的信号。使用高光谱图像，这种方法转化为基本光谱像素 (ESP) 和基本空间变量 (ESV) 的选择。这会产生高度精简的数据集，其好处是可以最大限度地减少计算工作量、细致的数据挖掘、更容易的模型构建以及更好的问题理解或解释。使用模拟和真实数据，我们表明 (i) 通常可以获得超过 99% 的减少率，