Estimation of correlated noise in hyperspectral images

摘要

Many hyperspectral image processing algorithms (e.g detection, classification, endmember extraction etc.) are generally designed with the assumption of no spectral or spatial correlation in noise. However, studies [1, 2] have shown the presence of non-negligible correlation between the noise samples in different spectral bands, especially between noise of adjacent bands. It was also shown recently [3] that many well-known endmember extraction algorithms e.g. [4] give poor estimates for the number of endmembers in the presence of correlated noise. This asks for a precise estimation of noise for cases where noise is spectrally correlated. We show in this paper that the commonly employed hyperspectral noise estimation algorithm based on regression residuals [5, 6, 4] is very affected by spectrally correlated noise and we suggest a modified approach that proves to be very robust to noise correlation. Simulation results show that the estimation error is reduced at times by a factor of 5 when there is high spectral correlation in the noise.