A Feature Selection Method via Graph Embedding and Global Sensitivity Analysis

Abstract

Feature selection has been a prominent research topic for a long time, not only in hyperspectral image classification but also in other related fields. It has gained even more popularity recently, especially with the growing interest in explainable AI studies. The literature on feature selection is extensively studied not only in remote sensing but also in the domain of computer science. However, most of the conventional approaches ignore information about the manifold structure of the data, which might be critical, especially for the analysis of hyperspectral data due to their complex nonlinear structure. This study introduces a feature selection approach based on graph embedding and global sensitivity analysis, utilizing the first-order terms of the high dimensional model representation. The effectiveness of the proposed method is analyzed on four hyperspectral datasets utilizing some evaluation criteria, including classification accuracy and clustering quality, and compared to seven state-of-the-art feature selection methods. The results show that the proposed method typically outperforms the others and is notably more computationally efficient.