基于低秩矩阵分解的遥感图像薄云去除方法

为了较好地去除遥感图像中的薄云遮挡,还原地表图像,提出了一种利用低秩矩阵分解的遥感图像薄云去除算法。该算法根据薄云图像的低秩特性,将图像进行低秩矩阵分解,得到背景、前景和薄云图像。然后将去除薄云信息后的前景和背景信息相融合,得到地物成分。将该算法与其他传统算法应用在卫星影像数据上,对薄云去除效果运用主客观指标进行比较。实验结果表明,该算法能够克服传统算法中细节丢失以及去除不完全的问题,不仅能够在不同地物场景下对薄云进行去除,而且能够较好地保留地物的细节信息。

Application of Low-Rank Matrix Decomposition in the Thin Cloud Removal from Remote Sensing Images

In order to remove the thin cloud from remote sensing images, a remote sensing image was decomposed using a low-rank matrix and the properties of the thin cloud was analysed. The images were decomposed into the parts of background, foreground and thin cloud. Then, aimed objects could be obtained by combining the background with foreground informations. Experiments using different algorithms were carried out on satellite images and the effects of cloud removal were measured by subjective and objective indexes. The experiment results illustrate that the method can eliminate the defects in conventional methods effectively. In different scenarios, it can not only effectively remove thin cloud from image of remote sensing efficaciously, but also retain the details of the objects.