Skip to Main content Skip to Navigation
Journal articles

Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images

Abstract : A maximum-likelihood method for estimating hyperspectral sensors random noise components, both dependent and independent from the signal, is proposed. A hyperspectral image is locally jointly processed in the spatial and spectral dimensions within a multicomponent scanning window (MSW), as small as 7 x 7 x 7 spatial-spectral pixels. Each MSW is regarded as an additive mixture of spectrally correlated fractal Brownian motion (fBm)-samples and random noise. The main advantage of the proposed method is its ability to accurately estimate band noise variances locally by using spatial and spectral texture correlations from a single textural MSW. For each spectral band, both additive and signal-dependent band noise components are estimated by linear fit of local noise variances obtained from many MSWs distributed over the whole band intensity range. CRLB-based analysis of the estimator performance shows that a good compromise is to jointly process seven adjacent spectral bands. The proposed method performance is assessed first on synthetic fBm-data and on real images with synthesized noise. Finally, four different AVIRIS datasets from 1997 flying season are considered. Good coincidence between additive and signal-dependent AVIRIS random noise components estimates obtained by our method and the estimates retrieved from AVIRIS calibration data is demonstrated. These experiments suggest that it is worth taking into account noise signal-dependency hypothesis for processing AVIRIS data.
Document type :
Journal articles
Complete list of metadatas
Contributor : Yolande Sambin <>
Submitted on : Friday, February 14, 2014 - 2:48:58 PM
Last modification on : Monday, October 5, 2020 - 9:50:06 AM



Mikhail Uss, Benoit Vozel, Vladimir Lukin, Kacem Chehdi. Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images. IEEE Journal of Selected Topics in Signal Processing, IEEE, 2011, 5 (3), pp.469-486. ⟨10.1109/JSTSP.2010.2104312⟩. ⟨hal-00946983⟩



Record views