高光谱成像
预处理器
全光谱成像
化学计量学
模式识别(心理学)
背景(考古学)
人工智能
空间分析
像素
数据处理
数据质量
计算机科学
光谱成像
数据预处理
成像光谱仪
遥感
数据挖掘
机器学习
分光计
光学
地质学
物理
古生物学
操作系统
生物
经济
公制(单位)
运营管理
作者
Alessandro Nardecchia,Raffaele Vitale,Eric Ziemons,Ludovic Duponchel
标识
DOI:10.1016/j.aca.2023.340805
摘要
Hyperspectral imaging technology is developing in a very fast way. We find it today in many analytical developments using different spectroscopies for sample classification purposes. Instrumental developments allow us to acquire more and more data in shorter and shorter periods of time while improving their quality. Therefore, we are going in the right direction as far as the measure is concerned. On the other hand, we can make a more mixed assessment for the hyperspectral imaging data processing. Indeed, the data acquired in spectroscopic imaging have the particularity of encoding both spectral and spatial information. Unfortunately, in chemometrics, almost all classification approaches today only use spectral information from three-dimensional hyperspectral data arrays. To be more precise, an approach encompassing the unfolding/refolding of such arrays is often applied beforehand because the majority of algorithms for analysing these data are not capable of handling them in their original structure. Spatial information is therefore lost during the chemometric exploration. The study of the spectral part of the acquired data array alone is clearly a limitation that we propose to overcome in this work. 2-D Stationary Wavelet Transform will be used in the data preprocessing phase to ensure the joint use of spectral and spatial information. Two spectroscopic datasets will then be used to evaluate the potential of our approach in the context of supervised classification.
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