Applications of Wavelet Penalty Function in De-noising for Hyperspectral Images

With the development of the hyperspectral remote sensing technology, the level of quantitative remote sensing has risen greatly, but varying degrees of random noise is contained. To optimize the use of the remote sensing images and to improve the effectiveness and accuracy of discriminating ground objects according to spectral absorption features, random noise removing is necessary. Aimed at the distribution characteristics of the random point noise of HJ_1A HSI data level 2 product, eliminate the Gaussian white noise by soft-thresholding filtering based on the Birge-Massart penalty function in wavelet domain. And the result shows that the method can not only remove the additive noise effectively, but also retain most of the feature information and edge details of the original image. Therefore, the method could provide necessary support for quantitative use of HJ_1A HSI data.