Wavelet threshold denoising for hyperspectral data in spectral domain

An improved method of wavelet threshold denoising is introduced and applied to hyperspectral imagery denoising in spectral domain. This method estimates a threshold value for each spectrum. Thresholds are set to a scalar specifying the percentage of cumulative power to retain in the filtered wavelet transform. Find the actual percent corresponding to these coefficients. During the processing, four families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a series of experiments to estimate the functioning of those wavelets and thresholding parameters. Experimental results show that the proposed algorithm with Coiflet provides an improvement in SNR for hyperspectral data specially.