# Multi-Source Pansharpening of Island Sea Areas Based on Hybrid-Scale Regression Optimization

## TL;DR

This paper introduces a new method to improve the clarity of satellite images of island sea areas by fusing data from multiple sources.

## Contribution

The novel Hybrid-Scale Mutual Information (HSMI) method improves pansharpening accuracy in island sea areas.

## Key findings

- HSMI improves spatial details and edge clarity of islands in satellite images.
- The method preserves the spectral characteristics of surrounding sea areas effectively.
- Comparisons with other methods show HSMI's superior performance in island and reef waters.

## Abstract

To address the demand for high spatial resolution data in the water color inversion task of multispectral satellite images in island sea areas, a feasible solution is to process through multi-source remote sensing data fusion methods. However, the inherent biases among multi-source sensors and the spectral distortion caused by the dynamic changes of water bodies in island sea areas restrict the fusion accuracy, necessitating more precise fusion solutions. Therefore, this paper proposes a pansharpening method based on Hybrid-Scale Mutual Information (HSMI). This method effectively enhances the accuracy and consistency of panchromatic sharpening results by integrating mixed-scale information into scale regression. Secondly, it introduces mutual information to quantify the spatial–spectral correlation among multi-source data to balance the fusion representation under mixed scales. Finally, the performance of various popular pansharpening methods was compared and analyzed using the coupled datasets of Sentinel-2 and Sentinel-3 in typical island and reef waters of the South China Sea. The results show that HSMI can enhance the spatial details and edge clarity of islands while better preserving the spectral characteristics of the surrounding sea areas.