Hyperspectral Image Feature Extraction Using Maclaurin Series Function Curve Fitting

Most of existing spectral-based feature extraction algorithms have gained increasing attention in hyperspectral image classification tasks. However, only original spectral is difficult to well represent or reveal intrinsic geometry structure of the image. In this paper, we construct the new features for each spectral response curve of hyperspectral image pixels, and then proposed a novel unsupervised nonlinear feature extraction algorithm that focuses on curve fitting and label-based discrimination analysis framework. In the algorithm, the coefficients of the fitted Maclaurin series function are considered as new extracted features in order to better capture the intrinsic geometrical nature of spectral response curves. Moreover, the algorithm can utilize the reflectance coefficients information of spectral response curves which has not been solved by many other statistical analysis based methods. The maximum likelihood classification results on two real-world hyperspectral image datasets have demonstrated the superiority of the proposed algorithm in image classification tasks.