高光谱遥感图像微分域三维混合去噪方法

高光谱遥感图像是一种三维数据,由二维空间信息和一维光谱信息组成.普通的对二维静态图像或一维光谱信息去噪的算法忽视了高光谱图像强烈的谱间相关性和图谱合一的特点,无法取得令人满意的效果.同时现代的高光谱遥感图像噪声级别相对较低,噪声方差随波段不同而不同.针对以卜特点,提出一种微分域三维混合去噪方法.首先将高光谱遥感图像变换到光谱微分域,使细微的噪声变得显著.然后在微分域中,对二维空间域采用基于小波的非线性阈值去噪BayesShrink算法.为克服噪声方差小同的特点,对光谱维不再采用小波阈值去噪方法,而采用Savitzky-Golay滤波进行平滑.最后对微分域去噪平滑处理后的图像进行光谱积分,并进行积分修正,消除光谱积分中引入的积累误差.对信噪比为600:1的机载可见红外成像光谱仪数据(AVIRIS)实验表明,该算法能有效地降低噪声,将信噪比提高到2 000:1以上.

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 spatial 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.