Fusion of Multispectral and Panchromatic Images via Spatial Weighted Neighbor Embedding

Abstract

Fusing the panchromatic (PAN) image and low spatial-resolution multispectral (LR MS) images is an effective technology for generating high spatial-resolution MS (HR MS) images. Some image-fusion methods inspired by neighbor embedding (NE) are proposed and produce competitive results. These methods generally adopt Euclidean distance to determinate the neighbors. However, closer Euclidean distance is not equal to greater similarity in spatial structure. In this paper, we propose a spatial weighted neighbor embedding (SWNE) approach for PAN and MS image fusion, by exploring the similar manifold structures existing in the observed LR MS images to those of HR MS images. In SWNE, the spatial neighbors of the LR patch are found first. Second, the weights of these neighbors are estimated by the alternative direction multiplier method (ADMM), in which the neighbors and their weights are determined simultaneously. Finally, the HR patches are reconstructed by the sum of HR patches corresponding to the LR patches multiplying with their weights. Due to the introduction of spatial structures in objective function, outlier patches can be eliminated effectively by ADMM. Compared with other methods based on NE, more reasonable neighbor patches and their weights are estimated simultaneously. Some experiments are conducted on datasets collected by QuickBird and Geoeye-1 satellites to validate the effectiveness of SWNE, and the results demonstrate a better performance of SWNE in spatial and spectral information preservation.