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. 2025 Jun 4;25(11):3530.
doi: 10.3390/s25113530.

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

Dongyang Fu  1   2 , Jin Ma  1   2 , Bei Liu  1   2 , Yan Zhu  1
Affiliations

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

Dongyang Fu et al. Sensors (Basel). .

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.

Keywords: South Sea Islands; mixed-scale regression; mutual information; pansharpening; sentinel remote sensing imagery.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
MTF-GLP-HPM-HSMI flowchart.
Figure 2
Figure 2
Schematic diagram of HPM injection scheme.
Figure 3
Figure 3
Visualization of the considered datasets made of coupled S3 OLCI (a) and S2 MSI scenes (b).
Figure 4
Figure 4
Visual presentation of the HY experiment (true color).
Figure 5
Figure 5
Visual presentation of the XB experiment (true color).
Figure 6
Figure 6
Visual presentation of the HG experiment (true color).
Figure 7
Figure 7
Subareas of full-band SAM average error for HY experiment. From left to right: (a) Selected subregions. (b) BDSD-PC. (c) C-GSA. (d) AWLP. (e) MTF-GLP. (f) MTF-GLP-HPM. (g) MTF-GLP-HPM-H. (h) MTF-GLP-HPM-R. (i) MTF-GLP-CBD. (j) MTF-GLP-Reg-FS. (k) TV. (l) RR. (m) MF. (n) FE-HPM. (o) MTF-GLP-HPM-HSMI.
Figure 8
Figure 8
Subareas of full-band SAM average error for XB experiment. From left to right: (a) Selected subregions. (b) BDSD-PC. (c) C-GSA. (d) AWLP. (e) MTF-GLP. (f) MTF-GLP-HPM. (g) MTF-GLP-HPM-H. (h) MTF-GLP-HPM-R. (i) MTF-GLP-CBD. (j) MTF-GLP-Reg-FS. (k) TV. (l) RR. (m) MF. (n) FE-HPM. (o) MTF-GLP-HPM-HSMI.
Figure 9
Figure 9
Subareas of full-band SAM average error for HG experiment. From left to right: (a) Selected subregions. (b) BDSD-PC. (c) C-GSA. (d) AWLP. (e) MTF-GLP. (f) MTF-GLP-HPM. (g) MTF-GLP-HPM-H. (h) MTF-GLP-HPM-R. (i) MTF-GLP-CBD. (j) MTF-GLP-Reg-FS. (k) TV. (l) RR. (m) MF. (n) FE-HPM. (o) MTF-GLP-HPM-HSMI.
Figure 10
Figure 10
The results of the 4-band fusion of the HY dataset are shown in standard false color (NIR–red–green).
Figure 11
Figure 11
Spectral errors of 21-band experiments on the HG dataset. From left to right: (a) Selected subregions. (b) BDSD-PC. (c) MTF-GLP-Reg-FS. (d) RR. (e) MTF-GLP-HPM-HSMI.
See this image and copyright information in PMC

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