Feature Selection for Thick Cloud Classification

Abstract

A method of feature selection based on hyperspectral data classification is proposed. The features are selected using iterative training of Decision Tree classifiers for further construction of a thick cloud classifier based on spectral features. Classifiers are trained with different hyperparameters for different set of features by recursive elimination. Feature selection is determined by analyzing the correlation between the decrease in Gini impurity and classification accuracy, combined with mean feature importance. Classification model training is performed for three types of surfaces: ocean, vegetation, and urbanized areas. Feature selection improves the accuracy of Random Forest classifier by choosing a limited set of features from the NIR and the lower part of the SWIR spectrum ranges according to their importance.