基于光谱融合的油漆分类方法比较

Comparison of Paint Classification Methods Based on Spectral Fusion

为了实现对油漆物证的快速、无损以及准确分类,实验收集了犯罪现场常见的5个油漆品牌共计50个油漆样本的红外原始光谱数据和导数光谱数据,结合光谱融合技术,建立了基于KNN、SVM以及逐步判别分析的油漆分类模型。实验结果表明:3种分类模型对于融合光谱的识别率要高于单一光谱;KNN以及SVM分类模型对于其中的3种油漆样本识别率高,但对其余2种样本的分类效果并不好,而逐步判别分析模型对5种油漆样本的各种光谱数据识别率均高于KNN和SVM模型,其中采用逐步判别分析中的Smallest F ratio判别模型对一阶导数光谱和三阶导数光谱融合数据的训练集和测试集实现了完全识别。本文方法的检验效率高,定性能力强,满足公安机关对于相关物证的快速检验要求,为刑事技术人员快速识别油漆物证提供了一种有效的手段。

To achieve fast,non-destructive,and accurate classification of paint evidence,the infrared raw and derivative spectral data of fifty paint samples from five common crime scenes were collected.The paint classification models based on KNN,SVM,and stepwise discriminant analysis were created using spectral fusion technology.The experimental results show that the three classification models for the fusion spectrum have a higher recognition rate than a single spectrum.The recognition rate of the KNN and SVM classification model for three paint samples is high,but the classification effect for the remaining two samples is not good.The recognition rate of the stepwise discriminant analysis completely model for all kinds of spectral data of five paint samples is higher than that of the KNN and SVM models.To achieve 100%recognition of the training and test sets,the Smallest F ratio discriminant model of stepwise discriminant analysis identifies the first derivative and third derivative spectral fusion data.This method has high efficiency and strong qualitative ability,and it meets the requirements for rapid inspection of relevant material evidence by public security organs.It also provides criminal technicians a quick way to identify paint evidence.