Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis
Keywords: Dimension reduction, Coordination factor analysis, Hyperspectral remote sensing, Feature construction, Manifold learning
Abstract. Over the past thirty years, the hyperspectral remote sensing technology is attracted more and more attentions by the researchers. The dimension reduction technology for hyperspectral remote sensing image data is one of the hotspots in current research of hyperspectral remote sensing. In order to solve the problems of nonlinearity, the high dimensions and the redundancy of the bands that exist in the hyperspectral data, this paper proposes a dimension reduction method for hyperspectral remote sensing image data based on the global mixture coordination factor analysis. In the first place, a linear low dimensional manifold is obtained from the nonlinear and high dimensional hyperspectral image data by mixture factor analysis method. In the second place, the parameters of linear low dimensional manifold are estimated by the EM algorithm of find a local maximum of the data log-likelihood. In the third place, the manifold is aligned to a global parameterization by the global coordinated factor analysis model and then the lowdimension image data of hyperspectral image data is obtained at last. Through the comparison of different dimensionality reduction method and different classification method for the low-dimensional data, the result illuminates the proposed method can retain maximum spectral information in hyperspectral image data and can eliminate the redundant among bands.