Denoising of hyperspectral imagery by combining PCA with block-matching 3-D filtering

Abstract

Even though the noise level in a hyperspectral data cube may be low, it still can affect most remote sensing applications. In this paper, a novel method is proposed for reducing noise in a hyperspectral data cube. The method first used principal component analysis (PCA) to decorrelate the useful information from noise and then applied block-matching and 3-D filtering to selectively reduce noise in the noisy PCA components. Finally, an inverse PCA was conducted to obtain a denoised data cube. The first few PCA components contained the majority of information in the hyperspectral data cube and very little noise. Because the method did not denoise the first few PCA components, most fine feature details in the data cube were retained after denoising. The signal-to-noise ratio after denoising the Greater Victoria Watershed District and the Cuprite data cubes improved significantly. From our experiments, the proposed method was very competitive when compared with other existing denoising methods published in the literature.