Under consideration that the profiles of bands at close wavelengths are quite similar and the curvelets are good at capturing profiles, a junk band recovery algorithm for hyperspectral data based on curvelet transform...Under consideration that the profiles of bands at close wavelengths are quite similar and the curvelets are good at capturing profiles, a junk band recovery algorithm for hyperspectral data based on curvelet transform is proposed. Both the noisy bands and the noise-free bands are transformed via curvelet band by band. The high frequency coefficients in junk bands are replaced with linear interpolation of the high frequency coefficients in noise-flee bands, and the low frequency coefficients remain the same to keep the main spectral characteristics from being distorted. Jutak bands then are recovered after the inverse curvelet transform. The performance of this method is tested on the hyperspectral data cube obtained by airborne visible/infrared imaging spectrometer （AVIRIS）. The experimental results show that the proposed method is superior to the traditional denoising method BayesShrink and the art-of-state Curvelet Shrinkage in both roots of mean square error （RMSE） and peak-signal-to-noise ratio （PSNR） of recovered bands.展开更多
基金Project(10871231) supported by the National Natural Science Foundation of China
文摘Under consideration that the profiles of bands at close wavelengths are quite similar and the curvelets are good at capturing profiles, a junk band recovery algorithm for hyperspectral data based on curvelet transform is proposed. Both the noisy bands and the noise-free bands are transformed via curvelet band by band. The high frequency coefficients in junk bands are replaced with linear interpolation of the high frequency coefficients in noise-flee bands, and the low frequency coefficients remain the same to keep the main spectral characteristics from being distorted. Jutak bands then are recovered after the inverse curvelet transform. The performance of this method is tested on the hyperspectral data cube obtained by airborne visible/infrared imaging spectrometer （AVIRIS）. The experimental results show that the proposed method is superior to the traditional denoising method BayesShrink and the art-of-state Curvelet Shrinkage in both roots of mean square error （RMSE） and peak-signal-to-noise ratio （PSNR） of recovered bands.
