With the emerging technology of remote sensing, a huge amount of remote sensing data is collected and stored in the remote sensin02222g platform, and the transmission and processing of data on the platform is extremely wasteful. It is essential to incorporate the speedy remote sensing processing services in an integrated cloud computing architecture. In order to improve the denoising ability of remote sensing image, a new structured dictionary-based method for multispectral image denoising based on cluster is proposed. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image is 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. The algorithm is applied to denoise the noisy remote sensing image of Maoergai area in the upper Minjiang which contain typical vegetation and soil is chosen as study area, simulation results show that higher peak-signal to noise ratio can be obtained as compared to other recent image denoising methods.

随着新兴的遥感技术的发展，在sensin02222g远程平台上收集并存储了大量的遥感数据，极大地浪费了平台上的数据传输和处理。必须将快速的遥感处理服务整合到集成的云计算体系结构中。为了提高遥感图像的去噪能力，提出了一种新的基于聚类的基于字典的结构化多光谱图像去噪方法。该方法兼顾了空间的局部性和多光谱图像跨光谱的相关性。通过聚类将遥感图像分为不同的组，并根据字典学习算法获得空间和频谱稀疏的表示系数以及字典。经过阈值处理后，对相似块进行平均，并通过多光谱遥感图像去噪实现。应用该算法对Min江上游毛尔盖地区典型植被和土壤的噪声遥感图像进行了去噪处理，研究结果表明，与其他近代图像相比，该方法可以获得较高的峰信噪比。去噪方法。