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A detection method for reservoir waterbodies vector data based on EGADS
Open Geosciences ( IF 2 ) Pub Date : 2020-12-13 , DOI: 10.1515/geo-2020-0205
Lingkui Meng 1 , Xiaobing Wei 1 , Miao Yang 1, 2 , Yizhuo Meng 1 , Yang Chen 3 , Jianguo Cheng 4 , Wen Zhang 1
Affiliation  

Abstract Owing to the effects of camera, illumination, extraction algorithm defect, and other reasons, vector data for reservoir waterbodies extracted from remote sensing data may have quality issues, impacting the efficiency of data utilization in areas such as water resource management and reservoir monitoring. To efficiently detect abnormal data from massive vector products of reservoir waterbodies, a semi-automatic detection method for reservoir waterbody vector data is presented. The method has three phases. First, the original reservoir vector data are preprocessed to obtain the time series of the area of reservoir waterbodies. Second, data modeling with time series of reservoir waterbodies area data is done using the extensible generic anomaly detection system (EGADS) plug-in framework and time series modeling is conducted using the Olympic model. Third, data that have quality problems are identified with K σ K\sigma model was used to determine the outliers; thereby, the date of the outliers is detected. Results of accuracy verification show that the sensitivity and specificity of the method were 94.44 and 83.87%, respectively, showing its feasibility for use in anomaly detection in polygonal reservoir waterbody vector data with far greater efficiency than traditional manual inspection.

中文翻译:

一种基于EGADS的水库水体矢量数据检测方法

摘要 由于相机、光照、提取算法缺陷等原因,从遥感数据中提取的水库水体矢量数据可能存在质量问题,影响水资源管理和水库监测等领域的数据利用效率。为有效检测水库水体海量矢量乘积中的异常数据,提出了一种水库水体矢量数据半自动检测方法。该方法分为三个阶段。首先对原始水库矢量数据进行预处理,得到水库水体面积的时间序列。第二,使用可扩展通用异常检测系统 (EGADS) 插件框架对水库水体区域数据的时间序列进行数据建模,并使用奥林匹克模型进行时间序列建模。第三,用KσK\sigma模型识别有质量问题的数据用于确定异常值;从而检测到异常值的日期。精度验证结果表明,该方法的灵敏度和特异性分别为94.44%和8​​3.87%,表明其在多边形水库水体矢量数据异常检测中的可行性,其效率远高于传统的人工检测。
更新日期:2020-12-13
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