Sun Lei, Luo Jian-Shu
College of Sciences, National University of Defense and Technology, Changsha 410073, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Oct;29(10):2717-20.
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. In the spectral derivative domain, considering that the noise variance is different from band to band, the spectrum is smoothed using Savitzky-Golay filter instead of wavelet threshold denoising method. Finally, the data smoothed in derivative domain are integrated along the spectral axis and corrected for the accumulated errors brought by spectral integration. The algorithm was tested on airborne visible/infrared imaging spectrometer (AVIRIS) data cubes with signal-to-noise ratio (SNR) of 600 : 1. Experimental results show that the proposed algorithm can reduce the noise efficiently, and the SNR is improved to more than 2 000 : 1.
为解决高光谱遥感影像的去噪问题，提出了一种导数域三维混合去噪算法。首先，将高光谱影像变换到光谱导数域，在该域中细微噪声水平会升高。然后在导数域，在二维空间域中采用基于小波的非线性阈值去噪方法——贝叶斯收缩算法。在光谱导数域，考虑到各波段噪声方差不同，使用Savitzky-Golay滤波器对光谱进行平滑处理，而非小波阈值去噪方法。最后，将在导数域中平滑后的数据沿光谱轴进行积分，并对光谱积分带来的累积误差进行校正。该算法在信噪比为600:1的机载可见/红外成像光谱仪（AVIRIS）数据立方体上进行了测试。实验结果表明，所提算法能有效降低噪声，信噪比提高到2000:1以上。
