Lithological classification by PCA-QPSO-LSSVM method with thermal infrared hyper-spectral data

Abstract

To improve the accuracy and operation efficiency of lithological classification using a thermal infrared airborne hyper-spectral imager (TASI) data, an innovative combinatorial algorithm (PCA-QPSO-LSSVM) based on principal component analysis (PCA), quantum-behaved particle swarm optimization (QPSO), and least-squares support vector machines (LSSVM) is proposed. After pre-processing, the emissivity data was extracted from TASI data, and 27 types of lithological units were selected in comparison with the measured spectrum and Johns Hopkins University spectrum library. The PCA was used for statistical analysis, and the appropriate n principal components were selected instead of the original 32 bands of TASI emissivity data. Based on the LSSVM classification algorithm, the improved QPSO algorithm was used to optimize the regularization parameter γ and the kernel function σ2 in the classification process. Finally, test samples selected from TASI data were classified by PCA-QPSO-LSSVM. The results show that, compared with the traditional LSSVM algorithm, PCA-QPOS-LSSVM has a higher recognition accuracy and greater operation efficiency. The field verification of the study area was carried out in LiuYuan town, GanSu province, China, and the accuracy of the field verification was 74.36% for the classification result, which is more consistent with the actual lithological distribution compared with the LSSVM algorithm. However, there are some flaws in this paper, such as the low classification accuracy in Moyite and plauenite. In general, The PCA-QPSO-LSSVM algorithm is a practical algorithm for lithological classification of TASI data.