【摘要】 受传感器特性影响,高光谱图像中的噪声在各个维度都有体现。噪声的存在降低了高光谱图像中信息的有效性,在进行地物分类前必须采用适当的算法对噪声予以去除。文章针对高光谱图像的噪声特性,提出了一种基于全变差的高光谱图像去噪算法。该算法将经典二维图像全变差去噪模型推广至三维形式,提出了采用双正则项及相应的调整参数的目标函数,在三维空间中完成新目标函数的离散化,并采用基于优化-最小化算法的迭代方法对目标函数进行优化与求解。对星载Hyperion成像光谱仪数据的实验表明,适当的设置调整参数,该方法可很好地提高高光谱图像的各波段信噪比、平滑光谱曲线并保留细节特征,其去噪效果优于经典的MNF去噪算法及Savitzky-Golay滤波方法。更多还原

【Abstract】 In the present paper,an effective total variation denoising algorithm is proposed based on hyperspectral imagery noise characteristics.The new algorithm generalizes the classical total variation denoising algorithm for two-dimensional images to a three-dimensional formulation.Considering the fact that the noise of hyperspectral imagery shows different characteristics in spatial domain and spectral domain respectively,the objective function of the proposed total variation algorithm is improved by utilizing double-regularizing terms(spatial term and spectral term) and separate regularization parameters respectively.Then,the new objective function is discretized via approximating the gradient of the regularizing terms by three orthogonal local differences,and further majorized by a convex quadratic function.Thus,noise in spatial and spectral domain could be removed independently by minimizing the majorizing function with a majorization-minimization(MM) based iteration.The performance of the proposed algorithm is experimented on a set of Hyperion imageries acquired in 2007.Experiment results show that,properly choosing the values of regularization parameters,the new algorithm has a similar improvement of signal-to-noise-ratio as minimum noise fraction(MNF) method and Savitzky-Golay filter,but a better performance in removing the indention and restoring the spectral absorption peaks.更多还原