Classification of hyperspectral remote sensing images using frequency spectrum similarity

Abstract

An algorithm of hyperspectral remote sensing images classification is proposed based on the frequency spectrum of spectral signature. The spectral signature of each pixel in the hyperspectral image is taken as a discrete signal, and the frequency spectrum is obtained using discrete Fourier transform. The discrepancy of frequency spectrum between ground objects’ spectral signatures is visible, thus the difference between frequency spectra of reference and target spectral signature is used to measure the spectral similarity. Canberra distance is introduced to increase the contribution from higher frequency components. Then, the number of harmonics involved in the proposed algorithm is determined after analyzing the frequency spectrum energy cumulative distribution function of ground object. In order to evaluate the performance of the proposed algorithm, two hyperspectral remote sensing images are adopted as experimental data. The proposed algorithm is compared with spectral angle mapper (SAM), spectral information divergence (SID) and Euclidean distance (ED) using the product accuracy, user accuracy, overall accuracy, average accuracy and Kappa coefficient. The results show that the proposed algorithm can be applied to hyperspectral image classification effectively.