针对相位敏感光时域反射计(Φ-OTDR)光纤预警系统对一维信号进行模式识别产生的误报和较低的识别效率, 提出基于形态学方法提取时空二维信号特征, 并利用相关向量机(RVM)分类器对事件进行分类识别的方法。首先, 将Φ-OTDR采集到的时空二维信号当作图像, 根据信号在图像上的特征采用图像处理的方法对不同入侵事件信号进行阈值分割。然后, 基于本文提出的特征提取方法, 利用不同事件区域在幅值、面积、形状以及区域间隔上的差别提取不同信号特征。最后, 利用相关向量机分类器对不同事件信号进行识别并采用"一对一"的多分类策略。对3种管道安全事件进行了实验。实验结果表明, 本文提出方法的识别精度能够达到97.8%, 而算法时间不到1 s。 与传统模式识别方法相比, 提出的算法大幅度地改善了系统性能, 且简便易行, 能够满足Φ-OTDR光纤预警系统在线实时监测的要求。

To reduce the time-consuming and misinformation of one dimensional signal recognition by the pre-warning system in a Phase-sensitivity Optical Time-domain Reflectometer(Φ-OTDR), a new method to acquire two dimension signals by the Φ-OTDR pre-warning system and to recognize events based on Relative Vector Machine(RVM) classifier was proposed. Firstly, the spatial and temporal two dimension signal was taken as an image and the image processing method was used for the threshold segmentation of different events according to the image characteristics. Then, the proposed feature extraction method based on morphology was used to extract different signal features by using the amplitude, area, shape and internal of region as feature vectors. Finally, the RVM classifiers and "one to one" strategy were used for multi-class recognition. The experiments on three pipeline safety events show that the feature extraction method proposed in this paper greatly improves the recognition accuracy with less computation time, the accuracy has been reached to 97.8% and the computing time is less than 1 s. As compared with traditional methods, the algorithm has better performance, thus is very suitable for the pre-warning system online monitoring of Φ-OTDRs.