近红外/短波红外光谱的矿物组分快速鉴定技术可以大大提高野外矿产勘查、 遥感矿物填图、 岩芯矿物组分分析等工作的效率, 成为目前高光谱技术研究的热点之一。 文章给出了一个基于光谱相似度评价约束的联合目标岩石样品光谱和矿物光谱端元库进行矿物组分光谱反演的统一模型, 然后以矿物光谱线性混合模型和光谱夹角相似度评价为例, 建立了一个具体的组分反演模型; 针对模型求解过程中的组合优化问题, 提出了一种人工免疫克隆选择计算的矿物组分光谱(ICSFSLIM)识别方法; 利用在中国新疆包古图地区选取的22个野外岩石样品的实测近红外/短波红外光谱进行了矿物组分提取试验, 以样品薄片鉴定结果为准, 将ICSFSLIM识别结果与组合特征光谱线性反演模型(CFSLIM)识别结果进行了定量的对比分析。 结果表明: ICSFSLIM比CFSLIM的识别正确率提高了2.26%, 有效率提高了18.6%, 并且具有更高的识别稳定性。

Rapid identification of minerals based on near infrared (NIR) and shortwave infrared (SWIR) hyperspectra is vital to remote sensing mine exploration, remote sensing minerals mapping and field geological documentation of drill core, and have leaded to many identification methods including spectral angle mapping (SAM), spectral distance mapping(SDM), spectral feature fitting(SFF), linear spectral mixture model(LSMM), mathematical combination feature spectral linear inversion model(CFSLIM) etc. However, limitations of these methods affect their actual applications. The present paper firstly gives a unified minerals components spectral inversion (MCSI) model based on target sample spectrum and standard endmember spectral library evaluated by spectral similarity indexes. Then taking LSMM and SAM evaluation index for example, a specific formulation of unified MCSI model is presented in the form of a kind of combinatorial optimization. And then, an artificial immune colonial selection algorithm is used for solving minerals feature spectral linear inversion model optimization problem, which is named ICSFSLIM. Finally, an experiment was performed to use ICSFSLIM and CFSLIM to identify the contained minerals of 22 rock samples selected in Baogutu in Xinjiang China. The mean value of correctness and validness identification of ICSFSLIM are 34.22% and 54.08% respectively, which is better than that of CFSLIM 31.97% and 37.38%; the correctness and validness variance of ICSFSLIM are 0.11 and 0.13 smaller than that of CFSLIM, 0.15 and 0.25, indicating better identification stability.