Evaluation of Dimensionality Reduction Methods for Remote Sensing Images Using Classification and 3D Visualization

Visual exploration is a natural way to understand the content of a data archive. If the data are multidimensional, the dimensionality reduction is an appropriate preprocessing step before the data visualization. In literature, two types of approaches are devoted to dimensionality reduction: feature selection and feature extraction algorithms. Both techniques intend to project a high dimensional space into a new one, with reduced dimensionality, preserving data inherent information. This paper aims to identify the similarity degree between low and high dimensional representations of a data archive using the optimal number of semantic classes as a criterion. This number is estimated based on the rate distortion theory being computed both before and after dimensionality reduction. The projection of the low dimensional space was obtained using one feature selection and six feature extraction methods.