A Remote Sensing Monitoring System for Marine Red Tides Based on Targeted Negative Sample Selection Strategies

The monitoring of harmful algal blooms (HABs) constitutes a vital component of marine environmental protection and the sustainable development of the marine economy. However, the highly dynamic nature of these small targets, compounded by the complex water color interference prevalent in the coastal waters where HABs frequently occur, has resulted in traditional remote sensing monitoring methods, particularly those relying on fixed spectral index thresholds and pixel-wise binarization, suffering from imprecise identification in turbid coastal waters where suspended sediments, cloud cover, and sun glint create spectral confusion. These methods also exhibit low automation due to manual threshold adjustment requirements and poor transferability across different spatiotemporal conditions. Consequently, these methods struggle to meet practical application requirements. This study establishes a U-net model-based remote sensing identification framework for red tides using HY-1D CZI imagery (50 m resolution, 1–3 day revisit), targeted negative sample strategies, and event-level accuracy validation methods to achieve efficient marine red tide detection. Targeted negative sample selection involves purposefully selecting spectrally ambiguous regions as negative samples, aiming to enhance recognition accuracy and sample selection efficiency. The combination of targeted sampling with deep learning enables portability to new spatiotemporal contexts by learning invariant spectral–spatial features rather than relying on scene-specific thresholds. Experimental results demonstrate that the targeted negative sample strategy reduces event-level model false negatives by 27%, false positives by 36%, and increases the F1 score by 0.3217. Using an identical sample size, the targeted sample selection strategy yields an F1 score 0.0479 higher than random sampling. To achieve equivalent recognition accuracy, an increased number of random samples would be required. Comparative experiments reveal that the proposed method enhances sample selection efficiency by 87.5%. Transferability is demonstrated through successful identification of red tide patches in Wenzhou waters on 13 April 2022, without model retraining. This demonstrates that red tide remote sensing recognition based on targeted sample selection enables efficient, precise, and automated identification without human intervention, providing a reliable technical solution for operational marine red tide monitoring.