一种用于机织物成分识别的新型高光谱成像与建模方法

摘要：纺织材料的成分识别对于纺织领域的质量控制和测量至关重要。提出了一种新的高光谱成像方法和相关的识别模型 来对单组分纺织品进行分类。首先，基于局部线性嵌入（LLE）、主成分分析（PCA）和局部保留投影（LPP）算法对单 组分织物的高光谱数据进行降维处理。此外，对920nm至2500nm的288个波长的原始数据进行压缩以保留典型波长区 域。之后，将这些数据输入两个分类器（（ 决策树分类器和K最近邻（（ KNN）分类器）进行训练，并基于这些训练数据开 发了用于样品分类的识别模型。实验结果表明，建立的识别模型可以正确识别所有样品。本研究证明了基于LPP模型和 KNN分类算法的分类器的识别率和稳定性具有最高的准确率。

关键词：高光谱成像 光谱分析 成分识别 降维 分类算法

期刊：Textile Research Journal

DOI：10.1177/0040517518821907

设备：GaiaSorter

A novel hyperspectral imaging and modeling method for the component identification of woven fabrics

Abstract：The component identification of textile materials is critical for quality control and measurement in the textile field. A novel hyperspectral imaging method and the related identification model are proposed to classify single-component textiles. Firstly, the hyperspectral data of the single-component fabrics were processed to conduct dimensionality reduction based on locally linear embedding (LLE), principal component analysis (PCA), and locally preserving projection (LPP) algorithms. Moreover, the original data of 288 wavelengths from 920nm to 2500nm were compressed to keep the typical wavelength regions. After that, these data were imported into two classifiers (decision tree classifier and K nearest neighbor (KNN) classifier) for training, and an identification model based on these training data was developed for the sample classification. The experimental results showed that all the samples could be identified correctly by the established identification model. The recognition rate and the stability of the classifier based on LPP model and KNN classification algorithm were proved to have the highest accuracy in our research.

Keywords：hyperspectral imaging, spectral analysis, component identification, dimension reduction, classification algorithm