基于改进的SVM技术和高光谱遥感的标准矿物定量计算

基于支持向量机(SVM)统计理论，并对其从核函数构造方面进行改进，通过主成分分析、包络线去除、光谱导数变换等对原始Hyperion高光谱数据进行降维、变换与特征提取，分析比较了这些变换后不同的回归效果，并将其应用在内蒙古霍林郭勒地区岩石中氧化物质量分数的反演中。同时，鉴于某些重要矿物本身并没有明显的特征光谱曲线，提出一种新的矿物定量方法。首先，基于高光谱遥感数据，利用改进的SVM回归技术反演矿物中的化学成分，然后通过标准矿物计算（CIPW）推导岩石中标准矿物的质量分数。研究结果表明：基于改进核函数后的SVM回归精度有所提高，其中导数变换后的反演精度达74.87%，比原始光谱反演精度提高了4.11%。CIPW应用于高光谱遥感地质填图效果良好，为岩性鉴定和评价提供了科学依据。

Standard Mineral Quantitative Calculation Based on the Improved SVM and Hyperspectral Remote Sensing

Based on SVM statistical theory,which was improved from kernel function’s construction,the original Hyperion hyperspectral data were dimensionally reduced,transformed and feature extracted by means of principle component analysis,envelope removal and spectra derivative transformation.The different regression results through the transformations were analyzed and compared.Then it was applied in the retrieval of rock’s oxide weight percent in Huolinguole,Inner Mongolia.A new mineral quantitative retrieval method was proposed for some important mineral which has not obvious characteristics of the spectral curve.Based on the hyperspectral remote sensing data,the chemical composition of mineral was inverted by improved SVM regression technology.Through the CIPW,the standard mineral percentage composition was derived.The results of the study show that:the SVM regression accuracy improved by improving the nuclear function,and the derivative of the transformed inversion precision up to 74.87%,improved 4.11% comparing to the original spectrum inversion precision.CIPW performed well in geological mapping using hyperspectral remote sensing,and provides a scientific basis on identification and evaluation of lithology.