Adaptive optimally segmentation of spectra for hyperspectral imagery classification

An adaptive dimensionality reduction method to conduct classification of hyper-spectral imagery using optimal segmentation of spectral signature is proposed. The method partitions the spectral signals into a fixed number of contiguous intervals with constant intensities in terms of minimizing the mean square error. To automatically obtain the best number of the segments, a quantitative indictor based on variables correlation between original and the reconstructed spectral approximation is designed, and the best segments can be adaptively determined by a user specified threshold. To validate the method, an experiment with aerial push-broom hyper-spectral imagery (PHI) is conducted, and the results demonstrate that the spectra data reduction using adaptive optimally segmentation can preserve the distinctions among spectral signatures and can improve the classification accuracy significantly. Comparisons with principal component analysis (PCA) and discrete wavelet transform (DWT) are also done, and the proposed method can achieve better classification accuracy with overall accuracy and kappa coefficient.