基于典型地物字典学习的遥感图像分块重构方法

遥感图像压缩的传统方法普遍存在着重构时间长、重构质量有待改进等应用难题。本文针对不同典型地物的遥感图像,采用K-SVD字典学习方法分别进行过完备字典训练。重构过程中,采用图像分块优化机制：首先对部分图像块通过多次迭代,从相应地物的过完备字典里求解出能线性表示原图像的原子;然后对其邻域内的图像块,优先使用这些原子中的一部分作为初始值求表示残差,以减少迭代次数。该方法充分利用了典型地物遥感图像的信息内容以及图像块间的相似性,在重构的图像质量、重构速度方面,与非冗余正交基构造的通用字典或未分类的学习字典相比,有一定优越性。

Remote-Sensing Image Block Reconstruction Algorithm Based on Typical Surface Features Dictionary Learning

As one of the hot issues of remote sensing imaging, the traditional method of remote sensing image compression has problems that widespread a long reconstruction time, and the quality of the reconstructed image needs to be improved. According to the remote sensing images of different typical surface feature, the K-SVD dictionary learning method is utilized in the paper. In the process of reconstruction, through multiple iterations on the part of the image blocks, the original image can be solved by a linear representation of the atoms from the corresponding surface feature of an overcomplete dictionary. Then the atoms are given preferentially as the initial value to calculate the residual of the image blocks in the neighborhood, to reduce the number of iterations. The remote sensing image information content on typical surface and the similarity between image blocks are fully exploited. Compared with the general dictionary structured by non-redundant orthogonal base or non-classified learning dictionary, the proposed method outperforms in the reconstructed image quality and reconstruction speed.