Considering Nonoverlapped Bands Construction: A General Dictionary Learning Framework for Hyperspectral and Multispectral Image Fusion

Abstract

Improving the spatial resolution of hyperspectral (HS) images is of great significance for the subsequent applications.1As the multispectral (MS) image can provide abundant complementary land-cover spatial information, hyperspectral and multispectral image fusion (HMF) have become a mainstream to generate HS images with both high spatial and spectral resolution. HMF has witnessed rapid progress by leveraging dictionary learning technique. However, existing approaches are highly sensitive to the image registration accuracy, and the reconstruction performance of the non-overlapped spectral bands between HS and MS image are extremely limited. To alleviate the effect of image misregistration and enrich the spectral information of non-overlapped bands, a general HMF dictionary learning framework which considers non-overlapped spectral bands reconstruction and image misregistration is proposed in this paper. For registration error, the proposed method is rectified by the improved dictionary learning, which can solve the problem of the spectral information matching gap existing in traditional HMF methods between HS image with MS image. Meanwhile, for non-overlapped spectral bands reconstruction, a novel coefficient optimization strategy is adopted to improve the non-overlapped bands reconstruction. Therefore, the registration error can be avoided to greatest extent and the accuracy of non-overlapped bands reconstruction can be effectively improved. Experiments both on simulated and real-world datasets demonstrate that the proposed method can effectively tackle the registration error problem and increase HMF accuracy with different spectral range. Meanwhile, the proposed framework provides guidance significance for the dictionary learning based HMF methods with various constrains to improve the non-overlapped bands reconstruction accuracy.