A study on spectral characteristics extraction using fourier approximation theory

In this article, based on the theory of function series approaching, we change the spectral dimension of the hyperspectral data by using the Discrete Fourier transformation, and get a new feature space which could show the shape point of the spectrum curve. The coefficient, which hyperspectral data's component in the new feature space has against the Fourier series, could tell us the effect of different spectral function to the shape of spectrum curve. The paper especially analyzes the possible effect of this feature space in image shadow recognition and precision improvement of unsupervised classification based on the Euclid distance, and verify via experiments.