基于结构性字典学习的毛儿盖遥感图像去噪研究

遥感图像的噪声分析、评估和滤波作为遥感图像处理的研究重点而一直受到遥感应用领域的关注。为了进一步提高遥感图像的去噪能力，提出一种新的基于聚类的组稀疏字典学习多光谱遥感图像去噪算法，该算法能够综合利用多光谱遥感图像的空间局部性和光谱的全局性，对遥感图像像素进行聚类后划分为不同的组，然后通过字典学习获得多光谱遥感图像的空间、光谱字典和系数。经过阈值处理后，对空间相似的块进行平均处理，实现了对多光谱遥感图像的去噪。该算法用于岷江上游植被和土壤类型典型地区——毛儿盖实验区遥感图像的去噪，峰值信噪比相比band-wise K-SVD算法提高了7.6%左右，同时具有更好的视觉效果。

Remote Sensing Image of Mao'ergai Denoising based on Structured Dictionary Learning

The noise analysis, evaluation and denoising of remote sensing image are the focus of RSI processing. In order to improve the denoising ability of remote sensing image, presents a new structured dictionary-based method for multispectral image denoising based on cluster. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image was divided into different groups by clustering, and sparse representation coefficients of spatial and spectral and dictionary is obtained according to the dictionary learning algorithm. After threshold processing, the similar blocks are averaged and realized with multispectral remote sensing image denoising. This algorithm is applied to the denoising of remote sensing image of typical vegetation and soil types in the upper reaches of Minjiang river- Maoergai experimental area. Compared with the band-wise K-SVD algorithm, the PSNR of this algorithm can be improved by about 7.6%, with better visual effect.