一种基于朴素贝叶斯分类模型的高光谱矿物精确识别方法

由于某些矿物，特别是与成矿作用有关的热液蚀变矿物的光谱特征差异较小，更受到矿物混合光谱等因素的影响，导致大多数光谱识别方法对一些光谱特征相似的矿物极易出现混淆和误判现象。因此，针对矿物光谱的“同物异谱”、“同谱异物”现象，提出了一种基于朴素贝叶斯分类模型的高光谱矿物精确识别方法。通过对白云母、高岭石，这两种光谱特征相近的典型蚀变矿物的实验测试、分析，并与光谱角匹配、二进制编码、光谱特征拟合等同类方法进行对比，结果表明该方法能够充分地利用吸收特征波谷位置、吸收特征深度、包络线斜率等多种矿物光谱识别属性特征，进而将不同种类的矿物更明显地予以区分，具有较高的分类识别准确率。

An Accurate Approach to Hyperspectral Mineral Identification Based on Naive Bayesian Classification Model

The spectral absorption features are very similar between some minerals, especially hydrothermal alteration minerals related to mineralization, and they are also influenced by other factors such as spectral mixture. As a result, many of the spectral identification approaches for the minerals with similar spectral absorption features are prone to confusion and misjudgment. Therefore, to solve the phenomenon of “same mineral has different spectrums, and same spectrum belongs to different minerals”, this paper proposes an accurate approach to hyperspectral mineral identification based on naive bayesian classification model. By testing and analyzing muscovite and kaolinite, the two typical alteration minerals, and comparing this approach with spectral angle matching, binary encoding and spectral feature fitting, the three popular spectral identification approaches, the results show that this approach can make more obvious differences among different minerals having similar spectrums, and has higher classification accuracy, since it is based on the position of absorption feature, absorption depth and the slope of continuum.