[Three-dimensional hybrid denoising algorithm in derivative domain for hyperspectral remote sensing imagery].

To tackle denosing problems in hyperspectral remote sensing imagery, a three-dimensional hybrid denoising algorithm in derivative domain was proposed. At first, hyperspectral imagery is transformed into spectral derivative domain where the subtle noise level can be elevated. And then in derivative domain, a wavelet based non-linear threshold denoising method, Bayes-Shrink algorithm, is performed in the two-dimensional spacial domain.

[Hyperspectral imagery denoising method based on wavelets].

To take advantage of the intrinsic characteristic of hyperspectral imageries, a hyperspectral imagery denoising method based on wavelet transform is proposed in the present paper. At first, two dimensional wavelet transform is performed on hyperspectral images band by band to capture their profiles. Due to the significant spectral correlation between adjacent bands, their high frequency wavelet coefficients are similar as well.