基于光谱融合的火星表面相关矿物分类方法研究

Classification and Discrimination of Martian-Related Minerals Using Spectral Fusion Methods

多源数据融合能在一定程度上扩展数据信息量,更利于建立准确和稳健的分析模型。行星探测中常采用多个载荷协同分析同一目标,因此利用多载荷数据融合辨别分析火星矿物具有重要科学意义和应用前景。分别采用可见近红外(Vis-NIR)反射光谱和拉曼(Raman)散射光谱两种技术手段测量了火星表面主要矿物(硅酸盐、硫酸盐、碳酸盐)的光谱特征曲线,并对获取的光谱数据进行基线校正、Savitzky-Golay平滑以及标准矢量归一化(SNV)等必要的数据预处理。根据光谱特征,首先选取样品Vis-NIR和Raman数据信息丰富、信噪比高、光谱信号重叠小的波段(Vis-NIR:430~2 430nm,Raman:130~1 100cm^(-1)),然后运用软独立建模分类法(SIMCA)、主成分分析法-K最邻近分类法(PCA-KNN)分别建立基于Vis-NIR,Raman及两者融合(累加融合、串联融合)的矿物聚类分析模型。采用SIMCA算法的矿物聚类准确率由单一光谱建模的72.6%(Vis-NIR),90.7%(Raman)提升为融合建模的96.3%(累加融合)和98.1%(串联融合);采用PCA-KNN的准确率由单一光谱建模的68.9%(Vis-NIR),72.9%(Raman)提升为融合后的80.3%(累加融合)和92.6%(串联融合)。实验结果表明:光谱融合能够发挥Vis-NIR,Raman各自的数据优势,所建火星表面相关矿物分类模型的预测准确度更高。该研究为我国火星探测任务奠定了岩石分类方法基础。

Multi-source data fusion is a powerful method to combine data from multiple sources to improve the potential values and interpretation performances of the source data. Multi-payload collaborative analysis is regularly used to detect the same target in planetary exploration. Therefore, it is of great significance and potential application to use spectral fusion to establish a more accurate and robust clustering analysis model for Martian minerals identification. In this paper, the spectral characteristics of the main Martian-related minerals were analyzed by using both visible near-infrared （Vis-NIR） reflectance spectroscopy and Raman spectroscopy. And some data pre-processing methods such as baseline correction, Savitzky-Golay smoothing, standard normal variate （SNV） scaling were used to produce a high-quality representation of the spectral data. Firstly, the information-rich spectral bands with higher signal-to-noise ratio and less overlapping were selected （i. e., Vis-NIR：430～2 430 nm; Raman：130～1 100 cm-1） for the clustering analysis. Secondly, soft independent method of class analogy （SIMCA） and principal component analysis-K-nearest neighbor （PCA-KNN）, were respectively built based on selected Vis-NIR, Raman and two kinds of their fusion data（i. e., coaddition fusion and concatenation fusion）, respectively. The accuracy of SIMCA model was enhanced from 72.6% （Vis-NIR） and 90.7% （Raman） to 96.3% （coaddition fusion） and 98. 1% （concatenation fusion）. The accuracy of PCA-KNN model was improved from 68.9% （Vis-NIR） and 72.9% （Raman） to 80.3% （coaddition fusion） and 92.6% （concatenation fusion）, respectively. The results indicate that the fused Raman/Vis-NIR data can improve the classification model’s accuracy of Martian-related minerals which will lay the foundation of quick rock classification for future Mars exploration.