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Mangrove species classification using novel adaptive ensemble learning with multi-spatial-resolution multispectral and full-polarization SAR images.
- Source :
- International Journal of Digital Earth; Jan2024, Vol. 17 Issue 1, p1-30, 30p
- Publication Year :
- 2024
-
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 fullpolarization 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 17538947
- Volume :
- 17
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- International Journal of Digital Earth
- Publication Type :
- Academic Journal
- Accession number :
- 178809104
- Full Text :
- https://doi.org/10.1080/17538947.2024.2346277