Mangrove species classification is particularly important for coastal wetland protection and global warming mitigation. However, it is challenging to identify species-level differences in practical applications. In the paper, we propose an object-oriented multi-feature ensemble classifier for the fine classification of mangrove species. First, mangrove images are segmented into objects by the multi-resolution segmentation method. Second, multiple features of each object are extracted by feature calculation methods (Gray Level Co-occurrence Matrix, Standard Deviation, Mean, etc.), and appropriate features are selected for species classification by weight estimation. Finally, the selected features are fed to an ensemble classifier to generate the final mangrove species classification results. Experiments performed on in-situ unmanned aerial vehicle (UAV) images collected in Yingzai, Guangdong Province demonstrate that the proposed multi-feature ensemble classifier achieves superior classification results to its single classifier counterparts.
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