生态与农村环境学报 ›› 2015, Vol. 31 ›› Issue (3): 425-431.doi: 10.11934/j.issn.1673-4831.2015.03.024

• 研究方法 • 上一篇    下一篇

基于像元质量分析的S-G滤波重建MODIS-NDVI

李明,沈润平,王迪,李鑫慧   

  1. 南京信息工程大学地理与遥感学院  
  • 收稿日期:2014-09-22 修回日期:2015-04-09 出版日期:2015-05-25 发布日期:2015-09-22
  • 通讯作者: 沈润平 南京信息工程大学地理与遥感学院 E-mail:rpshen@nuist.edu.cn
  • 作者简介:李明(1989—),男,江西赣州人,硕士生,主要研究方向为遥感建模与分析。E-mail:xgliminggnsy@126.com
  • 基金资助:

    国家重点基础研究发展计划(2010CB950701)

Reconstruction of MODIS-NDVI Using S-G Filtering Based on Pixel Quality Analysis

LI  Ming, SHEN  Run-Ping, WANG  Di, LI  Xin-Hui   

  1. School of Geography and Remote Sensing,Nanjing University of Information Science & Technology
  • Received:2014-09-22 Revised:2015-04-09 Online:2015-05-25 Published:2015-09-22
  • Contact: SHEN Run-Ping School of Geography and Remote Sensing,Nanjing University of Information Science & Technology E-mail:rpshen@nuist.edu.cn

摘要: 由最大值合成法得到的MODIS-NDVI 时间序列数据集被广泛用于植被信息提取,但该数据集仍含有噪声,影响对植被信息的提取效果。为了有效剔除噪声,提出一种滑动窗口内寻找噪声像元同类地物高质量像元,且用高质量像元均值替换噪声的Savitzky-Golay(S-G)滤波重建,再保留高质量像元的方法。该方法与自适应S-G 滤波 都较好地重建了2001—2003 年江西省MODIS-NDVI 时序数据。与自适应S-G 滤波的重建结果相比,新方法重建结果提高了与原始数据中高质量数据的相关性,降低了与原始数据噪声的相关性;噪声重建后与高质量数据均值和标准差更加接近;新方法能提高高质量像元的保真性与稳定性;基于像元质量分析S-G 滤波能重建得到较优的MODIS-NDVI 数据集,可以提取更加准确的植被覆盖度。

关键词: MODIS, NDVI, S-G 滤波, 时间序列

Abstract: MODIS NDVI products from maximum value composite (MVC) still contain noise pixels which may affect application of the data for extraction of vegetation information. In order to efficiently remove the noise, it is suggested that high quality pixels representing the same ground object as the noise pixels do be located within a sliding window and means of the high quality pixels be used to replace values of the noise pixels in reconstruction of Savitzky-Golay(S-G) filter, and then the high quality pixels be kept in storage. Both this method and self-adaptive S-G filter can be used effectively to reconstruct 2001 – 2003 MODIS-NDVI chronological data of Jiangxi Province. Compared to the self-adaptive S-G filter reconstruction. the new method improves the correlation of the reconstruction with the high quality portion of the original data, and lowers the correlation of the reconstruction with the noise in the original data; and the reconstructed noise data get much closer to the means of high quality data and standard deviation. Besides, the new method can enhance the fidelity and stability of high quality pixels. Based on pixel quality analysis, S-G filter can reconstruct and yield better MODIS-NDVI dataset and extract more accurate vegetation coverage.  

Key words: MODIS, NDVI, S-G filter, time series

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