Noise Analysis of Hyperspectral Images Captured by Different Sensors

Abstract

Noise usually appears in hyperspectral images (HSIs), and strongly affects the performance of the follow processing and analysis. In recent years, a large number of denoising algorithms have been proposed and it is known that the denoising effect is highly dependent on the accurate estimates of the type and level of noise present in an HSI. This paper focuses on analyzing the real noise in HSIs by separating and estimating the level of noise in HSIs. In consideration of the spectral correlation and the unique spatial structure of stripe noise, the developed method employs Fourier domain analysis and the high correlation among neighboring spectral bands to separate different types of noise. Experimental results show that the level of noise may be quite different for different bands of an HSI, and HSIs captured by different senors or in different scenes.