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Coastal wetland classification method based on UAV imagery: integrating hierarchical sample enhancement and multiscale sample selection techniques

Denghao Yang,Nan Zhou,Zhiyu Zhu,Huilin Ge,Wenchao Wang,Cheng Xu,Jing Zhang

Published 2025 in Geomatics, Natural Hazards & Risk

ABSTRACT

ABSTRACT Wetland vegetation is vital for maintaining ecosystem functions, necessitating precise mapping and dynamic monitoring for conservation. Traditional object-based image analysis (OBIA) struggles with subjective scale parameter selection in heterogeneous wetlands. This study proposes a novel scale-free classification framework tailored for coastal wetlands: (1) A scale-set structure is utilized to organize multiscale coastal wetland areas generated by region merging algorithms, avoiding inefficiency and subjectivity in selecting scale parameters during the segmentation phase. (2) A multiscale sample selection and enrichment strategy is adopted to ensure that the training model can learn multidimensional features from the macro- to microlevel, thereby enhancing the model's generalization ability and classification accuracy. (3) In terms of feature extraction, spectral features, index features, textural features, and geometric features are combined and optimized, further deepening the algorithm's understanding of the spatial distribution and layout patterns of wetland vegetation. Experiments have demonstrated significant improvements in segmenting mangroves, Spartina alterniflora, mudflats, and water bodies, with the overall accuracy increasing from 72% to 96%. The framework effectively addresses scale-related challenges in coastal wetland mapping, offering practical solutions for ecological restoration and conservation.

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