Dimensionality reduction and derivative spectral feature optimization for hyperspectral target recognition

Huge data volumes and redundant information are common problems in the field of hyperspectral target recognition. In this study, we propose a method to ensure the accuracy of target recognition while reducing the amount of data, where the effective bands in the hyperspectral data are selected for which the third-order derivative spectrum approaches zero. Next, a feature optimization method based on a combination of the derivative spectrum is proposed for hyperspectral target recognition, where a combination of the derivative spectrum and original spectrum is used as the basic vector after dimensionality reduction. The analyzed bands are decreased to reduce spectral interference and the data volume. The dimension of the combinatorial derivative spectrum is then increased to obtain more spectral information from the effective bands of the hyperspectral data. Thus, the proposed method can identify the target more accurately with fewer bands. Our experiments showed that the proposed method outperformed principal components analysis, local discriminant analysis, and kernel principal components analysis in low dimensions.