  The high-dimensional feature space of the hyperspectral imagery poses major
challenges to the processing and analysis of the hyperspectral data sets. In
such a case, dimensionality reduction is necessary to decrease the
computational complexity. The random projections open up new ways of
dimensionality reduction, especially for large data sets. In this paper, the
principal component analysis (PCA) and randomized principal component analysis
(R-PCA) for the classification of hyperspectral images using support vector
machines (SVM) and light gradient boosting machines (LightGBM) have been
investigated. In this experimental research, the number of features was reduced
to 20 and 30 for classification of two hyperspectral datasets (Indian Pines and
Pavia University). The experimental results demonstrated that PCA outperformed
R-PCA for SVM for both datasets, but received close accuracy values for
LightGBM. The highest classification accuracies were obtained as 0.9925 and
0.9639 by LightGBM with original features for the Pavia University and Indian
Pines, respectively.
