Water Anomaly Type Identification Based on Deep Multisphere Decision Boundaries Using Remote Sensing Data

The increasing frequency of water anomalies poses severe threats to aquatic ecosystem stability, water resource security, and socioeconomic sustainability. Rapid and accurate identification of these events is critical for early warning and informed environmental decision-making. However, existing type identification methods often fail to balance high precision with rapid inference since many depend on hand-crafted feature engineering or computationally intensive post-classification procedures that slow processing and increase confusion among anomalies with similar spectral characteristics. Therefore, in this study, a deep multisphere decision boundaries (DMSDB) method for unified, fine-grained identification of diverse water anomalies is developed. The method first involves constructing a compact 5-dimensional feature space by deriving physically interpretable indices from Sentinel-2 imagery. Then, a modified VM-UNet-v2 network is employed to extract water anomaly response features (WARFs) that capture the spatial–spectral characteristics of each anomaly. Finally, a multi-sphere-contrast loss function optimizes class-specific decision boundaries, compacting intraclass features while increasing the separation between spectrally similar anomaly categories. The validation results demonstrate that DMSDB achieves an average F1 score of 0.8789 and an mIoU of 0.7932, enabling efficient large-scale inference, and completing classification over a 10,000 km2 scene within 33 s. These results highlight the method’s potential for effective or ground-based rapid environmental monitoring, supporting timely and category-specific responses to aquatic disturbances.