LAUSR

Accurate estimation of the underlying noise is vital in the processing of hyperspectral images (HSIs). Previous studies have shown that many HSI processing algorithms perform poorly if the noise is not correctly estimated. The classic residual (CR) method is commonly employed for the estimation of variance of the noise in different spectral bands. However, noise estimates as per the CR method ignore the presence of spectral or spatial correlation amongst noise samples. Some studies have been conducted in the past to investigate the spectral and spatial correlation present in the noise but there are very few methods available to estimate the correlations present in the noise. In this paper, we present a reliable method for the estimation of the spectral correlation in the noise. Recently, it was shown that the CR method performs poorly when estimating the spectral correlation in the noise. By using both artificial and real datasets, we show that the proposed new method estimates the noise spectral correlation significantly more accurately than the CR method.