Improving Land Cover Class Separation Using an Extended Kalman Filter on MODIS NDVI Time-Series Data

被引：39
作者：
Kleynhans, Waldo [1 ,2 ]
Olivier, Jan Corne [1 ]
Wessels, Konrad J. [2 ]
van den Bergh, Frans [2 ]
Salmon, Brian P. [1 ,2 ]
Steenkamp, Karen C. [2 ]
机构：
[1] Univ Pretoria, Dept Elect Elect & Comp Engn, ZA-0002 Pretoria, South Africa
[2] CSIR, Remote Sensing Res Unit, Meraka Inst, ZA-0001 Pretoria, South Africa
来源：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2010年 / 7卷 / 02期
关键词：
Discrete Fourier transforms; Kalman filtering; CLASSIFICATION; ECOSYSTEM; IDENTIFICATION; BRAZIL; STATE; BRDF;
D O I：
10.1109/LGRS.2009.2036578
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
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.
引用
收藏
页码：381 / 385
页数：5
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