视频节点协同的雨量反演精度控制模型

Precision control model of rainfall inversion based on visual sensor nodes collaboration

广泛密布的视频传感器可持续记录降雨信息,基于视频传感器估算高时空分辨率的雨量数据,已经成为当前最具有前景的雨量估计途径之一。然而,由于传感器设备、视频场景等的复杂多变,极易导致各个视频传感器反演的降雨数据质量参差不齐,需要对其处理,保证反演数据质量。受地理学第一定律启发,以视频传感网中节点间的时空信息为约束,提出一种视频节点协同的雨量反演精度控制模型(Precision Control Model,PCM)。PCM模型通过视频节点间降雨信息互验证的方式,从降雨事件的时空一致性、态势一致性和相关性等特征出发,构建雨量反演的多粒度滤波方法,以期实现降雨事件的高精度表达。实验结果表明,在多种降雨场景中,PCM模型均可有效的提高了雨量反演的准确性与稳定性。降雨强度(Rainfall Intensity,RI)相对误差的均值在中、小雨场景降低约14.85%,大雨场景降低约19.90%;RI相对误差的标准差在中、小雨场景降低约40.87%,大雨场景降低约40.96%,可为高质量降雨数据的生产提供支持。

Widespread video sensors record rainfall information continuously.Video-based rainfall data estimation,with high spatio-temporal resolution,has become one of the most promising methods of rainfall data collection to date.However,due to the complexity and variability of sensor devices,video scenarios,etc.,the quality of rainfall data estimated can often contrast between individual visual sensors.Further processing is required to ensure the quality of rainfall inversion results.Inspired by Tobler's First Law of Geography,this study presents a precision control model(PCM)for video-based-rainfall inversion results correction.The model uses the spatio-temporal information between camera nodes,within the Visual Sensor Network,as the constraint.Rainfall events were analyzed from the dimensions of spatio-temporal consistency,situational consistency,and correlation,to achieve a high-precision representation of rainfall data.A multi-granularity filtering method was adopted for rainfall inversion using mutual verification of rainfall information among video nodes.The experimental results show that the PCM model can effectively improve rainfall inversion accuracy and stability in various rainfall scenarios.The mean value of the relative error of rainfall intensity(RI)is reduced by approximately 14.85%in light or medium rainfall scenarios,and approximately 19.90%in heavy or violent rainfall scenarios;For the standard deviation of the related error of RI,approximately 40.87%reduction for medium and light rain scenarios,and approximately 40.96%reduction for heavy rain scenarios.The results of this study confirm that the proposed PCM can provide support to produce high-quality rainfall data.