Spectral Similarity Measure Using Frequency Spectrum for Hyperspectral Image Classification

A novel spectral similarity measure approach, which is named spectral frequency spectrum difference (SFSD), is proposed for hyperspectral image classification based on the frequency spectrum of spectral signature using the Fourier transform. Many important characteristics of spectral signature can be clearly reflected in the frequency spectrum. Therefore, the spectral similarity is defined as the frequency spectrum´s difference between the target and reference signatures. The frequency spectrum analysis in this study suggests that the magnitude values of the first few low-frequency components for spectral signature can effectively represent the spectral similarity. To balance the difference between the low- and high-frequency components, the frequency spectrum of the target spectral signature is taken as the normalized factor in the SFSD method. Next, the U.S. Geological Survey spectral data and two hyperspectral remote sensing images were employed as test data in our validation experiments. The new SFSD proposed here was compared with the leading approaches in terms of the spectral discriminability and classification accuracy. Results show that the SFSD exhibits a relatively better performance and has more robust applications for hyperspectral image classification.