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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Improving Land Cover Class Separation Using an Extended Kalman Filter on MODIS NDVI Time-Series Data
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Improving Land Cover Class Separation Using an Extended Kalman Filter on MODIS NDVI Time-Series Data

机译:使用扩展的卡尔曼滤波器对MODIS NDVI时间序列数据改善土地覆盖物类别的分离

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摘要

It is proposed that the normalized difference vegetation index time series derived from Moderate Resolution Imaging Spectroradiometer satellite data can be modeled as a triply (mean, phase, and amplitude) modulated cosine function. Second, a nonlinear extended Kalman filter is developed to estimate the parameters of the modulated cosine function as a function of time. It is shown that the maximum separability of the parameters for natural vegetation and settlement land cover types is better than that of methods based on the fast Fourier transform using data from two study areas in South Africa.
机译:建议将中分辨率成像光谱仪卫星数据导出的归一化植被指数时间序列建模为三重(均值,相位和幅度)调制余弦函数。其次,开发了非线性扩展卡尔曼滤波器,以估计作为时间函数的调制余弦函数的参数。结果表明,使用来自南非两个研究区域的数据,自然植被和居民点土地覆盖类型的参数最大可分离性优于基于快速傅里叶变换的方法。

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