A comparative evaluation of filter-based feature selection methods for hyper-spectral band selection

Band selection (dimensionality reduction) plays an essential role in hyper-spectral image processing and applications. This article presents a unified comparison framework for systematic performance comparison of filter-based feature selection models and conducts a comparative evaluation of four methods: maximal minimal associated index (MMAIQ), mutual information-based max-dependency criterion (mRMR), relief feature selection (Relief-F), and correlation-based feature selection (CFS) for hyper-spectral band selection. The evaluation is based on the performance of effectiveness, robustness, and classification accuracy, which involves five measuring indices: class separability, feature entropy, feature stability, feature redundancy, and classification accuracy. Three images acquired by different sensors were used to investigate the performance of the metrics. Experimental results show the best results for MMAIQ for all data sets in terms of used measurements, except for feature stability where mRMR and Relief-F exhibit their superiority.