Frequency Domain-Based Features for Hyperspectral Image Classification

Abstract

Frequency spectrum has been proven to have the potential in hyperspectral image classification and ground object recognition. The characteristics of the frequency spectrum, such as dc component, descent rate, and spectrum oscillation, are different from each other; thus, based on the discrepancy in the frequency spectrum, 14 frequency spectrum features, including frequency spectrum integration area, spectral centroid ( $C_{k}$ and $C_{k-\textrm {log}}$ ), spectral rolloff ( $C_{t}$ ), spectral flux, spectral gradient of peaks, and valley, number of crosspoint, and first three peaks and valleys position, are proposed. To evaluate the performance of the proposed features, two commonly used hyperspectral images were taken as experimental data sets. Then, we employed three frequently used classification methods to perform the experiment based on spectral-only and frequency-spectral features. The results show that the proposed features can distinctly prompt the classification accuracies by combining the original spectral features.