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1. BAND-SPECIFIC SHEARLET-BASED HYPERSPECTRAL IMAGE NOISE
REDUCTION
ABSTRACT
Hyper spectral images (HSIs) can be very noisy, andthe amount of noise may differ from
band to band. While somespectral bands may be dominated by low signal-independent
noiselevels, others have mixed noise levels, which may include highlevels of Gaussian, Poisson,
and Spike noises. When a denoising algorithm is globally applied to the whole data set, it
usuallyaffects the low-noise bands adversely. Therefore, it is better to use different criteria for
denoising different bands. In this paper, we propose a new denoising strategy to do so. The
method is based on a 2-D non-subsample shearlet transform, applied to each spectralband of the
HSI. We propose an effective method to distinguishbetween bands with low levels of Gaussian
noise (LGN bands) andbands with mixed noise (MN bands) based on spectral correlation.LGN
bands are denoised using a thresholding technique on theshearlet coefficients. On the MN bands,
a local noise reductionmethod is applied, in which the detail shearlet coefficients of adjacent
LGN bands are employed. This targeted approach is prone toreduce spectral distortions during
denoising compared with global denoising methods. This advantage is shown in experiments
wherethe proposed method is compared with state-of-the-art denoisingmethods on synthetic and
real hyperspectral data sets. To assess the effect of denoising, classification and spectral
unmixing task sare applied to the denoised data. Obtained results show the superiorityof the
proposed approach.