A PAN-sharpening algorithm using gradient constraints and Laplacian regularization via total variation

Shih-Shuo Tung; Yu-Lin Tsai; Peng-Yu Chen

doi:10.1117/12.3039224

20 December 2024 A PAN-sharpening algorithm using gradient constraints and Laplacian regularization via total variation

Shih-Shuo Tung, Yu-Lin Tsai, Peng-Yu Chen

Author Affiliations +

Proceedings Volume 13266, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII; 1326604 (2024) https://doi.org/10.1117/12.3039224
Event: Asia-Pacific Remote Sensing, 2024, Kaohsiung, Taiwan

Abstract

The aim of PAN-sharpening is to fuse a Multispectral (MS) image and a Panchromatic (PAN) image into a High-Resolution Multispectral (HRMS) image. The spatial resolution of the HRMS image is extracted from the PAN image, while the spectral resolution is extracted from the MS image. In this paper, a PAN-sharpening model based on the gradient constraint and Laplacian regularization is proposed. The objective function, which is a convex optimization problem, aims to minimize three least-square terms: (1) spectral constraint, (2) spatial constraint, and (3) image regularization. In experiments, the proposed method not only demonstrates better visual quality but also shows improvement in many quality metric evaluations.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shih-Shuo Tung, Yu-Lin Tsai, and Peng-Yu Chen "A PAN-sharpening algorithm using gradient constraints and Laplacian regularization via total variation", Proc. SPIE 13266, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII, 1326604 (20 December 2024); https://doi.org/10.1117/12.3039224

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KEYWORDS

Image quality

Image enhancement

Algorithm development

Convex optimization

Image visualization

Mathematical optimization

Visualization

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