Dimension reduction and feature space analysis on chang'e-2 celms data for mare basalt units classification

The brightness temperature (T-B) features extracted from Chang'e Lunar Microwave Sounder (CELMS) data have been proved their superiority to study mare basalt. In this paper, dimension reduction and feature space analysis are conducted on T-B features to fully understand the data distribution and reduce the feature redundancy in the classification process based on two methods - Principal Component Analysis (PCA) and Nonnegative Matrix Factorization (NNMF). The results showed that PCA and NNMF can effectively enhance the classification capability of early(?)- and late(?)-age mare basalt respectively, and proved the necessity of dimension reduction for CELMS T-B features due to the largely-existing redundancy.