ABSTRACT

Sparse representation (SR)-based pansharpening methods which combine the dictionary and estimated sparse coefficients have achieved visually and quantitatively great results in pansharpening problem these days. And the details injection (ID)-based methods can receive comparable images by sharpening the multispectral bands through adding the proper spatial details from panchromatic (PAN) images. Recently, method based on sparse representation of injected details (SR-D) which combines the SR and ID points out a new way forward for pansharpening. In this direction, manifold regularized sparse representation of injected details (MR-SR-D) which introducing a manifold regularization (MR) into the former SR-D model have improved the quality of pansharpened images greatly utilizing a graph Laplacian to incorporate the locally geometrical structure of the multispectral data. However, due to the lack of spatial information in PAN, and the use of higher-order features similarity with the original multispectral images, the resulting images still have spatial and spectral distortion. Thus, in this paper, we propose a new method to enhance the spatial resolution of aiming image by adding the weighted local geometrical structure of PAN and multispectral images, and improve the spectral resolution by joining the higher-order structure connection between multispectral and aiming images to the MR-SR-D method which can be called as weighted manifold regularized (WMR) sparse representation of featured injected details method (WMR-SR-FD). Experimental results using IKONOS, QuickBird and WorldView2 data show that the proposed method can achieve remarkable spectral and spatial quality.

摘要

近来，结合字典和稀疏系数的基于稀疏表示（SR）的全清晰度方法已经在视觉和定量上取得了很大的效果。通过添加来自全色（PAN）图像的适当空间细节，通过锐化多光谱波段，基于细节注入（ID）的方法可以接收可比较的图像。近年来，基于注入细节的稀疏表示（SR-D）的方法结合了SR和ID，指出了一种新的泛锐化方法。在这个方向上，注入细节的流形正则化稀疏表示（MR-SR-D）将流形正则化（MR）引入前SR-D模型，极大地提高了锐化图像的质量，这是通过使用拉普拉斯图来合并多光谱的局部几何结构而实现的数据。但是，由于PAN中缺少空间信息，并且使用了与原始多光谱图像相似的高阶特征，因此所得图像仍然具有空间和光谱失真。因此，在本文中，我们提出了一种通过添加PAN和多光谱图像的加权局部几何结构来提高目标图像空间分辨率的新方法，通过将多光谱和瞄准图像之间的高阶结构连接加入MR-SR-D方法来提高光谱分辨率，该方法可称为特征注入细节方法（WMR-SR-FD）的加权流形正则化（WMR）稀疏表示）。使用IKONOS，QuickBird和WorldView2数据进行的实验结果表明，该方法可以实现出色的光谱和空间质量。