基于机器学习的寒区渠道冰情的遥感监测方法

Monitoring channel ice conditions in cold regions using remote sensing and machine learning

寒区渠道冬季运行时常出现冰情,控制平封的封冻过程会大幅降低渠道输水能力,调控不当甚至可能产生冰塞、冰坝等灾害。国内外开展了大量渠道冰情研究,以期提升渠道冰期输水能力,但受限于观测资料的时空密度,数值模拟结果难以验证,调度决策缺少依据。遥感技术因其具有监测范围大、时效性高的特性,在渠道冰情监测中具有较大的应用潜力。为探索适用于寒区渠道冰情遥感监测的方法,该研究以南水北调中线京石段明渠段为研究区,基于Sentinel-2影像的11个波段反射率构建了完全特征、优选特征和组合特征3类特征空间数据集,作为支持向量机(support vector machine,SVM)、最大似然估计(maximum likelihood estimation,MLE)、随机森林(random forest,RF)分类算法输入,训练得到了9个地物分类器,用于渠道结冰范围识别,并采用北拒马闸前影像渠道结冰范围提取试验,对比不同分类算法和输入特征组合下的分类性能。结果表明:在渠道结冰范围识别中,近红外、可见光和短波红外是关键波段。在样本数量有限的条件下,SVM算法结冰范围识别精度最高,不同特征输入下制图精度(producer’s accuracy,PA)可达85.10%~87.91%,错分误差(commission error,CE)为10.84%~16.08%;RF算法在完全特征和优选特征输入下分类精度与SVM接近,PA为84.67%~86.61%,CE为13.76%~14.41%,但其在组合特征下分类结果严重偏离实际;MLE算法在3类特征下的分类精度均较低,不适宜作为渠道结冰范围识别算法。综合来看,SVM算法对特征空间敏感性较低,在不同的特征输入下均能实现渠道结冰范围的高精度提取;RF算法对特征空间敏感性较高,当输入特征发生变化时,结冰范围识别精度不稳定。最后以完全特征下的SVM算法为例,进行了分类器的时空泛化性验证,结果表明模型在不同时间、不同渠段下,制图精度不低于82.09%,错分误差不高于13.82%,分类模型精度均较好,能有效识别渠道结冰范围。该研究方法可为寒区输水工程冰情监测提供新思路,亦可为类似工作提供参考。

Channels often freeze in cold regions in winter.A control system of horizontal freezing always causes a significant decrease in the water delivery capacity of channels.Improper regulation may even lead to disasters,such as ice jams and ice dams.A large number of studies on the channel ice conditions have been conducted to improve the water delivery capacity and safety during the ice period.However,it is still lacking in the scheduling decisions from the verified numerical simulation,particularly for the spatiotemporal density of observation data.Remote sensing has great potential for the channel ice monitoring,due to the wide range of observation and high timeliness.This study aims to explore the suitable method for the remote sensing monitoring of channel ice conditions in cold regions.The open channel was taken from the Beijing-Shijiazhuang section in the middle route of the south-to-north water transfer project.Three types of feature space datasets were constructed using 11 bands of Sentinel-2 images,including the complete feature,the optimized feature,and the combined feature.These datasets were selected as the inputs for the classification of the support vector machine(SVM),maximum likelihood estimation(MLE),and random forest(RF).Nine classifiers were trained to identify the freezing range of the channel.An experiment was conducted to extract the freezing range of the channel from the images of the Beijuma check-gate.The performance of classification was compared under different classifications and inputs.The results indicated that the nearinfrared(NIR),visible light(R,G,and B),and shortwave infrared(SWIR)were the key bands to recognize the range of the channel freezing.The highest accuracy was achieved in the SVM using the limited sample size,with a producer's accuracy(PA)of 85.10%-87.91%and a commission error(CE)of 10.84%-16.08%under different feature inputs;The accuracy of RF classification was close to that of SVM using complete and optimized feature,with PA=84.67%-86.61%,and CE=13.76%-14.41%.But the classification was seriously deviated from the reality under combined features;The MLE classification shared the low accuracy under all three types of features,indicating unsuitable for the recognition of channel icing range.Overall,the SVM had a low sensitivity to the feature space,whereas,the high-precision observation was achieved in the range of channel freezing under different feature inputs;The RF shared a high sensitivity to feature space,where the unstable accuracy was observed when the input features changed.Taking the SVM with complete features as an example,the spatiotemporal generalization of the classifier was finally verified for the model.The lowest mapping accuracy of 82.09%and the highest misclassification error of 13.82%were achieved at different times and channel segments.The classification model with the better accuracy can be expected to effectively identify the icing range of the channel.The finding can provide a new approach to monitoring the ice conditions in water transfer projects in cold regions.The application scenarios of satellite remote sensing can also be extended to the strong reference in the field of ice monitoring.