High-resolution fine-grained wetland mapping based on class-balanced deep semantic segmentation networks

Wetlands are among the most valuable environmental resources and are crucial to achieving sustainable development strategies. However, the number, distribution, and types of wetlands are poorly understood. Widely used medium-resolution wetland products do not provide enough surface feature for fine-grained wetland mapping. To fill this gap, a high-resolution wetland remote sensing mapping dataset covering the contiguous United States was constructed. On this dataset, the performance of deep semantic segmentation networks with various backbones and architecture for wetland mapping was evaluated. Several loss functions were implemented to address the issue of class imbalance of this dataset, resulting in an improvement in classification accuracy. High spatial resolution images offer an abundance of surface texture, shape, structure, and neighborhood relationship data that can be applied to the classification of large-scale wetlands. The combination of high-resolution imagery and deep semantic segmentation models enables the automatic classification of wetlands to be refined.