Michishita, Ryo等：Empirical comparison of noise reduction techniques for NDVI time-series based on a new measure

Empirical comparison of noise reduction techniques for NDVI time-series based on a new measure
作者:Michishita, R (Michishita, Ryo)[ 1 ] ; Jin, ZY (Jin, Zhenyu)[ 1 ] ; Chen, J (Chen, Jin)[ 1 ] ; Xu, B (Xu, Bing)[ 1,2,3 ]
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷: 91  页: 17-28
DOI: 10.1016/j.isprsjprs.2014.01.003
出版年: MAY 2014

摘要
This study empirically compared noise reduction techniques for the normalized difference vegetation index (NDVI) time-series based on a new absolute measure using a time-series of 16-day composite NDVI images extracted from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products covering the Poyang Lake area in China. We proposed an approach to accurately extract representative NDVI temporal profiles for the 12 land cover cluster types by clustering profiles, selecting optimal number of clusters, merging and labeling clusters, and selecting the representative NDVI profiles. The geometric average of the mean average distance between the reconstructed profile and the raw profiles, and the mean average distance between the reconstructed profile and the upper envelope (D-g(nr, c)) was selected as the most appropriate measure substitutive to RMSE for the evaluation of the noise reduction effects, when the 'true' profiles were not available. The running median, mean value, maximum operation, end point processing, and Hanning smoothing (RMMEH) filter and iterative Savitzky-Golay filter were the two most appropriate noise reduction techniques for the NDVI temporal profiles of the study area in the evaluation of noise reduction effects by the seven techniques. The robust framework using the proposed approach for the accurate extraction of representative NDVI temporal profiles and (D-g(nr,c)) in this study, is applicable in the evaluation of noise reduction effects using different techniques and in other study areas. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

通讯作者地址: Xu, B (通讯作者)
Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China
[ 3 ] Univ Utah, Dept Geog, Salt Lake City, UT 84112 USA