Integrated PCA-FLD method for hyperspectral imagery feature extraction and band selection

An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details that are needed for the subsequent detection, discrimination and classification processes. Band selection and combination not only serves as the first step of hyperspectral data processing that leads to a significant reduction of computational complexity, but also a invaluable research tool to identify optimal spectra for different online applications. In this paper, an integrated PCA and Fisher linear discriminant (FLD) method is proposed for hyperspectral feature band selection and combination. Based on tests in a hyperspectral detection application, this new method achieves better performance than other feature extraction and selection methods in terms of robust classification