Locally linear embedding based example learning for pan-sharpening

In this paper, a novel example based method is proposed to solve the remote sensing pan-sharpening problem, utilizing an implicit non-parametric learning framework. The high resolution (HR) and down-sampled panchromatic (PAN) images are used to train the high/low resolution patch pair dictionaries. Based on the perspective of locally linear embedding (LLE), every patch in each multi-spectral (MS) image band is modeled by its K nearest neighbors in patch set generated from low resolution (LR) PAN image, and this model can be generalized to the HR condition. The intended HR MS patch is reconstructed from the corresponding neighbors in HR PAN patches. Finally, the HR MS images are recovered by stitching these patches together. Two datasets of images acquired by Quick-Bird satellite are used to test the performance of the proposed method. Experimental results show that the proposed method performs well in preserving spectral information as well as spatial details.