A variational pan-sharpening algorithm to enhance the spectral and spatial details. (3rd July 2021)

• Record Type: Journal Article
• Authors: Gogineni, Rajesh; Chaturvedi, Ashvini; B S, Daya Sagar
• Is Part Of: International journal of image and data fusion. Volume 12:Number 3(2021)
• Page Start: 242
• Page End: 264
• Publication Date: 2021-07-03
• Subjects: Pan-sharpening -- total generalised variation -- inter-band correlation -- convex optimisation -- variational method -- alternating direction method of multipliers; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2020.1838629
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: Pan-sharpening is a remote sensing image fusion technique that generates a high-resolution multispectral (HRMS) image on combining a low resolution multispectral (MS) image and a panchromatic (PAN) image. In this paper, a new optimisation model is proposed for pan-sharpening. The proposed model consists of three terms: (i) a data synthesis fidelity term formulated on inferring the relationship between source MS image and fused image to preserve the spectral information, (ii) a total generalised variation-based prior term to inject the significant spatial details from PAN image to pan-sharpened image, and (iii) a spectral distortion reduction term that exploits the correlation between multispectral image bands. To solve the resultant convex optimisation problem, an efficient and convergence guaranteed operator splitting framework based on the alternating direction method of multipliers (ADMM) algorithm is formulated. Finally, the proposed model is experimentally validated using full-resolution and reduced-resolution data. The pan-sharpened outcomes exhibit the potential of the proposed method in enhancing the spatial and spectral quality.