An assessment of mineral identification applying hyperspectral infrared imagery in laboratory conditions is presented here and strives to identify nine different minerals (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, quartz). A hyperspectral camera in Long-Wave Infrared (LWIR, 7.7–11.8 $\mu$m) with a LW-macro lens, an infragold plate, and a heating source are instruments used in the experiment. For automated identification, a Sparse Principal Component Analysis (Sparse PCA)-based K-means clustering is employed to categorise all pixel-spectra in different groups. Then the best representatives of each cluster (using spectral averaging) are chosen to compare these spectra to ASTER spectral library of JPL/NASA through spectral comparison techniques. Spectral angle mapper (SAM) and Normalized Cross Correlation (NCC) are two of such techniques, which are used herein to measure the spectral difference. In order to evaluate robustness of clustering results among the minerals spectra, we have added three levels of Gaussian and salt&pepper noise, $0\mathrm{\%},2\mathrm{\%}$, and $4\mathrm{\%}$, to input spectra which dropped the accuracy percentage from more than 84.73$\mathrm{\%}$, for 0$\mathrm{\%}$ added noise, to 44.57$\mathrm{\%}$, for 2$\mathrm{\%}$ average of both additive noise, and 22.21$\mathrm{\%}$, for 4$\mathrm{\%}$ additive noise. The results conclusively indicate a promising performance but noise sensitive behaviour of the proposed approach.

本文介绍了在实验室条件下使用高光谱红外图像进行矿物鉴定的评估，力求鉴定出九种不同的矿物（黑云母，透辉石，山竹，针铁矿，蓝晶石，白钨矿，史密松石，电气石，石英）。长波红外（LWIR，7.7–11.8$\mu$m）实验中使用的仪器是带有LW-微距镜头，红外金板和加热源。对于自动识别，基于稀疏主成分分析（Sparse PCA）的K-means聚类用于将不同类别的所有像素光谱分类。然后，选择每个群集的最佳代表（使用光谱平均），以通过光谱比较技术将这些光谱与JPL / NASA的ASTER光谱库进行比较。频谱角度映射器（SAM）和归一化互相关（NCC）是其中两种技术，在本文中用于测量频谱差异。为了评估矿物光谱之间的聚类结果的鲁棒性，我们添加了三个高斯和盐和胡椒噪声级别，$0\mathrm{％}，2\mathrm{％}$和 $4\mathrm{％}$，输入光谱，使准确度百分比从84.73起下降$\mathrm{％}$，为0$\mathrm{％}$ 噪音增加至44.57$\mathrm{％}$，用于2$\mathrm{％}$ 附加噪声的平均值和22.21$\mathrm{％}$，用于4$\mathrm{％}$加性噪声。结果最终表明该方法具有良好的性能，但对噪声敏感。