Abstract
Joint Spectral and Spatial Consistency Priors for Variational Pansharpening
Highlights
As a challenging research task in remote sensing, pansharpening is to fuse a low resolution (LR) multispectral (MS) image together with a high resolution (HR) panchromatic (Pan) image of the same earth scene to produce a pan-sharpened HR MS image, which has both high spatial and spectral resolutions
This paper proposes a novel variational method with joint spectral and spatial consistency priors for pansharpening, which is very different with my previous work [26], [27], which exploited the total generalized variation (TGV)-induced spatial difference prior [26] and the spectralspatial low-rank prior [27] for pansharpening
LOCAL SPECTRAL CONSISTENCY FIDELITY TERM First, the relation between the HR MS and LR MS images will be modeled for preserving spectral information
Summary
As a challenging research task in remote sensing, pansharpening is to fuse a low resolution (LR) multispectral (MS) image together with a high resolution (HR) panchromatic (Pan) image of the same earth scene to produce a pan-sharpened HR MS image, which has both high spatial and spectral resolutions. Some TV-based spatial consistency prior terms were proposed to enforce these gradient-based spatial consistency constraints, which well preserved image edges sharp but caused the staircase effects in the pan-sharpened MS images. More effective Hessian feature-based spatial consistency prior models as well as spectral-spatial prior models will be investigated for spatial information transferring and spectral information preserving together in this paper To this end, this paper proposes a novel variational method with joint spectral and spatial consistency priors for pansharpening, which is very different with my previous work [26], [27], which exploited the TGV-induced spatial difference prior [26] and the spectralspatial low-rank prior [27] for pansharpening. (2) Under the forward-backward splitting framework, an optimization algorithm which efficiently solves the proposed model is designed
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