Bayesian fusion of multispectral and panchromatic images using a multi-mode and multiorder gradient tensor-based l1/2 sparse model

ABSTRACT

In this letter, based on the tensor representation modelling, we propose a multi-mode and multi-order gradient tensor-based non-convex model (M²GTNM) for Bayesian fusion of multispectral (MS) and panchromatic (Pan) images, which aims at producing the high-resolution MS (HRMS) images. Specifically, by modelling the MS image as the order-3 tensor, we mainly develop the multi-mode and multi-order gradient tensor sparse priors of MS image for fusion. For the spectral preservation of low-resolution MS (LRMS) image, the spectral fidelity constraint between HRMS and LRMS images is imposed. For the spatial-mode prior modelling, the multi-order spatial gradient tensor-based non-convex $l_{1/2}$ sparse prior between HRMS and Pan images is particularly imposed. Moreover, for the spectral-mode prior modelling, the spectral gradient tensor-based non-convex $l_{1/2}$ sparse prior between HRMS and upsampled LRMS images is further imposed. Then, we apply the alternating direction method of multipliers to optimize the proposed model. Finally, the reduced-scale and full-scale fusion experiments both validate the effectiveness of M²GTNM method.

KEYWORDS:

• Bayesian fusion of multispectral and panchromatic images
• tensor modelling
• spectral-mode gradient tensor
• spatial-mode gradient tensor
• $l_{1/2}$ sparse model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the China Postdoctoral Science Foundation under Grant [2022M711692], the Natural Science Foundation for Colleges and Universities in Jiangsu Province under Grant [23KJB520026], the National Natural Science Foundation of China under Grant [61802202], and the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications under Grant [NY222107].