摘要： 传统的海洋锋识别方法依赖于梯度阈值，其将梯度值大于设定阈值的海域视为存在海洋锋，但梯度阈值法存在阈值依赖人为设定且标准不统一，以及复杂多样的海洋锋无法用单一阈值进行准确识别的问题。为此，提出一种融合深度学习的自适应梯度阈值判别方法。对海温梯度图进行标注，通过Mask R-CNN训练得到海洋锋像素级识别模型，统计每一类锋特有的梯度值分布作为该类锋的基准梯度阈值，并基于该阈值对像素级的锋面识别结果做精细化调整，对锋面识别结果精度进行量化，以提高自适应锋面调整过程的可靠性。实验结果表明，与传统梯度阈值法及单一的深度学习结果相比，该方法可以实现精细的海洋锋识别，且具有良好的独立性和完整性。

关键词: 海洋锋识别, 自适应梯度阈值法, 锋面识别精度, 图像实例分割, 深度学习

Abstract: Traditional oceanic front identification depends on the gradient threshold,and sea areas with a gradient value greater than the set threshold are regarded as ocean fronts.However,the threshold is set artificially according to inconsistent standards,and complex ocean fronts cannot be accurately identified based on a single threshold.To address the problems,this paper proposes an adaptive gradient threshold recognition method for ocean fronts based with deep learning.It annotates the sea temperature gradient map,and obtains a model that can identify ocean fronts at the pixel level through Mask R-CNN training.The unique gradient value distribution of each type of front is counted as the benchmark gradient threshold of the front,and based on this threshold the pixel-level front recognition results are finely adjusted.The accuracy of the front recognition results is quantified to improve the reliability of adaptive front adjustment process.Experimental results show that compared with the traditional gradient threshold method and pure deep learning,this method can realize fine ocean front recognition automatically,and has good independence and integrity.

Key words: ocean front recognition, adaptive gradient threshold method, front recognition accuracy, image instance segmentation, deep learning

中图分类号: 

• TP391