Noise estimation in a noise-adjusted principal component transformation and hyperspectral image restoration

Abstract

We apply a noise-adjusted principal component transformation (NAPCT) to an Earth Observing 1 (EO-1) Hyperion image whose noise structure is typically unknown. In this paper, we propose to simulate and estimate the noise covariance structure of either a body of water, such as an ocean or lake, or a horizontal piece-wise delineation along a spatially homogeneous area. The effect is compared to that of the near-neighbor difference method utilized in some of the literature. A strategy is proposed of efficiently and accurately locating the noisy bands, particularly the striping bands and the striping columns. It automates the task of manual examination of each band and is particularly useful for hyperspectral data. We illustrate algorithmically that the implementation of NAPCT can be achieved by application of the procedure in linear discriminant analysis (LDA). The resultant images of NAPCT are compared to those from standard principal component transformation (PCT). By using the first 10 NAPCT bands (almost striping and noise free), which explain 99.8% of total data variability, we can reproject the NAPCT image back onto the original spectral space for visualization and image enhancement. The quality of the restored hyperspectral image is greatly improved.