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The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery

机译:使用状态估计技术将湿地覆盖物变化动态应用于时间序列遥感影像

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ABSTRACT Monitoring the dynamics of inundation areas in wetlands over contiguous years is important because it influences wetland ecosystem monitoring. However, because the variable nature of wetlands tends to hamper monitoring change analyses, the potential for misinterpretation increases. The Kalman filter (KF) or extended Kalman filter (EKF), which uses recursive processing based on the former information, can be applied to time-series remote sensing imagery. In the experiment, a periodic triangle function of two modulated parameters is treated as the system model, and Normalized Difference Vegetation Index (NDVI) time-series data are used for the measurement model in the correction processes of the state estimation. A decision metric is computed from the mean and amplitude sequence, which results from the state estimation filter. Consequently, an optimal threshold is calculated using a minimum error thresholding algorithm based on a pre-labelled sample. NDVI time-series data from Poyang Lake, China?????¢????????derived from 250-m Moderate Resolution Imaging Spectroradiometer satellite data obtained from January 2009 to December 2013?????¢????????are applied to monitor the dynamics of inundation changes. The results show that the EKF achieves satisfactory results, with 85.52% accuracy in the year 2009, while the KF has an accuracy of 84.16% during that same time.
机译:摘要连续多年监测湿地中淹没区域的动态很重要,因为它会影响湿地生态系统的监测。但是,由于湿地的多变性质往往会妨碍对变化分析的监测,因此误解的可能性增加了。基于前者信息进行递归处理的卡尔曼滤波器(KF)或扩展卡尔曼滤波器(EKF)可以应用于时间序列遥感影像。在实验中,将两个调制参数的周期三角函数作为系统模型,并在状态估计的校正过程中将归一化植被指数(NDVI)时间序列数据用作测量模型。根据均值和幅度序列计算决策指标,这是由状态估计滤波器得出的。因此,基于预先标记的样本,使用最小错误阈值算法来计算最佳阈值。来自中国Po阳湖的NDVI时间序列数据是根据2009年1月至2013年12月获得的250米中分辨率成像分光辐射计卫星数据得出的。应用于监视淹没变化的动态。结果表明,EKF取得了令人满意的结果,2009年的准确度为85.52%,而KF的同期准确度为84.16%。

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