Large-Scale Tidal Wetland Classification Based on Label Augmentation and Error Correction

Tidal wetlands, situated crucially at the land-sea intersection, play a pivotal role in sustainable development. Remote sensing has become a useful method for large-scale tidal wetland mapping. However, it encounters challenges due to tidal wetlands exhibit considerable similarity in spectral characteristics and show significant seasonal variations. The existing machine-learning-based methods are difficult to achieve high-efficiency mapping, hence restricting their utility in wetland monitoring. Conversely, deep learning methods possess a significant edge in managing intricate data, resulting in better classification accuracy. However, the requirement of pixel-level labels for deep learning has posed difficulties for extensive network training. In this article, a framework based on historical point samples is introduced for large-scale tidal wetland mapping. The proposed method uses existing point labels to generate per-pixel pseudo-labels, maximizing the utilization of historical data, and utilizing methods such as CutMix and bootstrapping loss to deal with label noise.