Feature extraction method based on multifractal parameters for hyperspectral imagery

Multi-fractal parameter extraction method based on spectral probability measurement was proposed to resolve the problem that the local absorption characteristics of hyperspectral data can not be described by the single fractal dimension. The method of spectral information measurement was used to calculate the spectral probability. The scaling function was estimated with the partition function. The differential coefficient of scaling function was calculated to obtain Holder exponent, and the multi-fractal spectrum was computed with Legendre transformation of scaling function. Four multi-fractal parameters can be extracted from multi-fractal spectrum and Holder exponent. The minimum Euclidean distance rule with the characteristic extraction based on multi-fractal parameters was applied to hyperspectral image supervised classification. The hyperspectral image was collected by airborne push-broom hyperspectral imager (PHI). The applied results show that the efficiency and reliability of the proposed method and its classification accuracy are about 94.789%, which is better than the classification accuracy of information fractal dimension and multi-fractal spectrum.