By Topic

Change detection of MODIS time series using a wavelet transform

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Yingchao Piao ; Comput. Network Inf. Center, Beijing, China ; Baoping Yan ; Shan Guo ; Yanning Guan
more authors

This paper presents a rapid and easy-to-use approach for change detection of NDVI time series using wavelet transform. Wavelet transform has a long history in signal and image processing field. However, the research on the large-scale remote sensing images by wavelet transform is rarely. The method in this paper is practical by using wavelet analysis on the large scale remote sensing time series. The time series based on Normalized Difference Vegetation Index (NDVI) are fundamental to analyze the dynamic nature of the vegetation. The trend of NDVI time series is extracted by using wavelet transform. For more accurate results, a seeded region growing algorithm is used for the detailed study on the certain areas. Results from the Tibetan Plateau show the wavelet transform in combination with the region growing procedure provides a efficient approach to estimate the areas where vegetation changed.

Published in:

Systems and Informatics (ICSAI), 2012 International Conference on

Date of Conference:

19-20 May 2012