Comparison assessment of low rank sparse-PCA based-clustering/classification for automatic mineral identification in long wave infrared hyperspectral imagery

Abstract The developments in hyperspectral technology in different applications are known in many fields particularly in remote sensing, airborne imagery, mineral identification and core logging. The automatic mineral identification system provides considerable assistance in geology to identify mineral automatically. Here, the proposed approach addresses an automated system for mineral (i.e. pyrope, olivine, quartz) identification in the long-wave infrared (7.7–11.8 μm - LWIR) ground-based spectroscopy. A low-rank Sparse Principal Component Analysis (Sparse-PCA) based spectral comparison methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Normalized Cross Correlation (NCC) have been used to extract the features in the form of false colors composite. Low-rank Sparse-PCA is used to extract the spectral reference which and showed high similarity to the ASTER (JPL/NASA) spectral library. For decision making step, two methods used to establish a comparison between a kernel Extreme Learning Machine (ELM) and Principal Component Analysis (PCA) kernel K-means clustering. ELM yields classification accuracy up to 76.69% using SAM based polynomial kernel ELM for pyrope mixture, and 70.95% using SAM based sigmoid kernel ELM for olivine mixture. This accuracy is slightly lower as compared to clustering which yields an identification accuracy of 84.91% (NCC) and 69.9% (SAM). However, the supervised classification significantly depends on the number of training samples and is considerably more difficult as compared to clustering due to labeling and training limitations. Moreover, the results indicate considerable similarity between the spectra from low rank approximation from the spectra of pure sample and the spectra from the ASTER spectral library.