Simultaneous dimensionality reduction and denoising of hyperspectral imagery using bivariate wavelet shrinking and principal component analysis

Abstract

In this paper, we propose a method that not only reduces the dimensionality of a hyperspectral data cube but also removes noise in the data cube by combining the bivariate wavelet thresholding with principal component analysis (PCA). The data cube thus obtained is applied to mineral endmember extraction and mineral detection. The reason why we incorporate bivariate wavelet denoising into PCA dimensionality reduction is because the dimensionality-reduced channels using PCA often contain significant amounts of noise. By reducing noise in the data cube, we can get better dimensionality-reduced output channels for hyperspectral data analysis and processing. Experiments reported in this paper confirm that the proposed method outperforms PCA for endmember extraction and mineral detection using the Cuprite data cube.