Humans are transforming land cover at an ever-increasing rate. Accurate geographical maps on land cover, especially rural and urban settlements are essential to planning sustainable development. Time series extracted from MODerate resolution Imaging Spectroradiometer (MODIS) land surface reflectance products have been used to differentiate land cover classes by analyzing the seasonal patterns in reflectance values. The proper fitting of a parametric model to these time series usually requires several adjustments to the regression method. To reduce the workload, a global setting of parameters is done to the regression method for a geographical area. In this work we have modified a meta-optimization approach to setting a regression method to extract the parameters on a per time series basis. The standard deviation of the model parameters and magnitude of residuals are used as scoring function. We successfully fitted a triply modulated model to the seasonal patterns of our study area using a non-linear extended Kalman filter (EKF). The approach uses temporal information which significantly reduces the processing time and storage requirements to process each time series. It also derives reliability metrics for each time series individually. The features extracted using the proposed method are classified with a support vector machine and the performance of the method is compared to the original approach on our ground truth data.

人类正在以越来越高的速度改变土地覆盖率。准确的土地覆盖地理地图，特别是农村和城市住区，对于规划可持续发展至关重要。从MODerate分辨率成像光谱仪（MODIS）地表反射率产品中提取的时间序列已用于通过分析反射率值的季节性模式来区分土地覆盖类别。参数模型对这些时间序列的正确拟合通常需要对回归方法进行一些调整。为了减少工作量，对地理区域的回归方法进行了全局参数设置。在这项工作中，我们修改了元优化方法，以设置回归方法来按时间序列提取参数。模型参数的标准偏差和残差的大小用作评分函数。我们使用非线性扩展卡尔曼滤波器（EKF）成功地将三重调制模型拟合到研究区域的季节性模式。该方法使用时间信息，这大大减少了处理时间和存储要求以处理每个时间序列。它还可以分别导出每个时间序列的可靠性指标。使用支持向量机对使用提出的方法提取的特征进行分类，并将该方法的性能与原始方法的地面真实数据进行比较。该方法使用时间信息，这大大减少了处理时间和存储要求以处理每个时间序列。它还可以分别导出每个时间序列的可靠性指标。使用支持向量机对使用提出的方法提取的特征进行分类，并将该方法的性能与原始方法的地面真实数据进行比较。该方法使用时间信息，这大大减少了处理时间和存储要求以处理每个时间序列。它还可以分别导出每个时间序列的可靠性指标。使用支持向量机对使用提出的方法提取的特征进行分类，并将该方法的性能与原始方法的地面真实数据进行比较。