Mangrove species classification using novel adaptive ensemble learning with multi-spatial-resolution multispectral and full-polarization SAR images

ABSTRACT

Mangroves are one of the important components of Earth's carbon sinks. The current problems of base-model composition strategy of ensemble learning and image features combination are still major challenges in mangrove species classification. This paper constructed two novel adaptive ensemble learning frameworks (AME-EL and AOS-EL) to explored the effect of combing different spatial-resolution optical and SAR images on classification performance, and evaluated the ability in mangrove species classification between dual-polarization and full-polarization SAR images. Finally, we used the SHAP method to explore the effects of different feature interactions on mangrove species classification. The results indicated that: (1) AME-EL and AOS-EL achieve the fine classification of mangrove species with overall accuracies between 77.50% and 94.77%. (2) Combination of Gaofen-7 multispectral and Gaofen-3 SAR improved the classification accuracy for Kandelia candel, with the F1 score increasing from 26.4% to 40.2%. (3) The VV/VH polarization performed better in the classification, with the F1 scores for Aegiceras corniculatum and Kandelia candel were higher than those of HH/HV and AHV polarization by 7%−16.1% and 5.9%−16.1%, respectively. (4) SAR features interacted well with other spectral features, which made a strong contribution to the classification accuracy of mangrove species, and effectively affect the prediction results.

KEYWORDS:

• Mangrove species classification
• adaptive ensemble learning
• base-model composition strategy
• optical and SAR combination
• feature interactions

Acknowledgements

We appreciate the anonymous reviewers for their comments and suggestions, which helped to improve the quality of this manuscript. And we are grateful to Prof.Wang of the University of Rhode Island to checked and modified the language expression and scientific contents of this paper.

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (grant number 42371341, 42004006), the Natural Science Foundation of Guangxi Zhuang Autonomous Region (Grant number 2024GXNSFAA010351), the Innovation Project of Guangxi Graduate Education (Grant Number YCSW2023353), and the ‘BaGui Scholars’ program of the provincial government of Guangxi (grant Number 2019A30), and in part by Zhejiang Province ‘Pioneering Soldier’ and ‘Leading Goose’ R&D Project (grant Number 2023C01027), the Guilin University of Technology Foundation (grant number GUTQDJJ2017096).