运用经验模态分解和压缩感知方法进行遥感影像超分辨率重建

Super-resolution Reconstruction of Remote Sensing Images by Using Empirical Mode Decomposition and Compressed Sensing

遥感影像的超分辨率重建在提高遥感影像的地物识别能力、不同空间分辨率遥感影像融合等方面具有重要的意义。在前人研究的基础上,结合影像经验模态分解(Empirical Mode Decomposition,EMD)、压缩感知理论和主成分变换方法,实现彩色影像的超分辨率重建。算法运用EMD方法首先得到影像的高频成分,然后通过K-SVD学习方法得到过完备字典,运用MOP(Orthogonal Matching Pursuit,正交匹配追踪)方法重构影像。在此基础上,对多光谱影像进行PCA变换,利用第一主成分(PC1)进行字典学习,将得到的字典运用于多光谱影像的超分辨率重建,得到超分辨率的彩色影像。以Geoeye-1全色和多光谱影像为例,验证方法的有效性。

Super resolution （SR） of remote sensing images is significant for improving accuracy of target identification and for image fusing. Conventional fusion-based methods inevitably result in distortion of spectral information,a feasible solution to the problem is the single-image based super resolution. In this work,we proposed a single-image based approach to super resolution of muhiband remote sensing images. The method combines the EMD （Empirical Mode Decomposition） ,compressed sensing and PCA to dictionary learning and super resolution reconstruction of remote sensing color image.First, the original image is decomposed into a series of IMFs（Intrinsic Mode Function） according to their frequency component by using EMD,and the super resolution is implemented only on IMFl,which includes high-frequency compo- nent;then the K-SVD algorithm is used to learn and obtain overcomplete dictionaries, and the MOP （Orthogonal Matching Pursuit） algorithm is used to reconstruct the IMF1 ;Finally,the up-scaled IMF1 is com- bined with other IMFs to acquire the super resolution of original image.For a multiband image reconstruction, a PCA transform is first implemented on multiband image, and the PC1 is adopted for learning to get overcomplete dictionaries,the obtained dictionaries is then used to super-resolution reconstruction of each multi-spectral band.The Geoeye-1 panchromatic and multi-spectral images are used as experimental data to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed method is workable to exhibit the detail within the images.