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首页> 外文期刊>Journal of Spectral Imaging >Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data
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Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

机译：可见近红外高光谱成像数据分类模型开发中光谱选择方法的比较

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Author Summary: Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.
机译：作者摘要：近年来，高光谱成像（HSI）在样品的定量和定性测量中的应用已经广泛增长，这主要归因于成像光谱仪器的性能提高和成本降低。数据采样是高光谱图像分析中至关重要但经常被忽略的步骤，这会影响后续结果及其解释。在选择用于分类模型校准的像素光谱时，可以利用HSI数据中的空间信息。在本文中，提出了多种用于选择像素光谱的采样策略，并通过五个案例研究进行了举例说明。根据捕获的全局可变性的比例，实用性和预测模型性能对策略进行比较。使用变异函数分析作为采样之前空间分割的指南，可以选择代表性的子集，同时可以减少模型性能参数在重复随机选择中的变化。

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