Please use this identifier to cite or link to this item: https://doi.org/10.1109/TGRS.2009.2029095
Title: Unsupervised change detection for satellite images using dual-tree complex wavelet transform
Authors: Celik, T. 
Ma, K.-K.
Keywords: Bayesian inference
Change detection
Difference image
Dual-tree complex wavelet transform (DT-DWT)
Environmental monitoring
Expectation maximization (EM)
Gaussian mixture modeling
Multispectral images
Multitemporal images
Remote sensing
Satellite images
Surveillance
Unsupervised thresholding
Issue Date: 2010
Source: Celik, T., Ma, K.-K. (2010). Unsupervised change detection for satellite images using dual-tree complex wavelet transform. IEEE Transactions on Geoscience and Remote Sensing 48 (3 PART 1) : 1199-1210. ScholarBank@NUS Repository. https://doi.org/10.1109/TGRS.2009.2029095
Abstract: In this paper, an unsupervised change-detection method for multitemporal satellite images is proposed. The algorithm exploits the inherent multiscale structure of the dual-tree complex wavelet transform (DT-CWT) to individually decompose each input image into one low-pass subband and six directional high-pass subbands at each scale. To avoid illumination variation issue possibly incurred in the low-pass subband, only the DT-CWT coefficient difference resulted from the six high-pass subbands of the two satellite images under comparison is analyzed in order to decide whether each subband pixel intensity has incurred a change. Such a binary decision is based on an unsupervised thresholding derived from a mixture statistical model, with a goal of minimizing the total error probability of change detection. The binary change-detection mask is thus formed for each subband, and all the produced subband masks are merged by using both the intrascale fusion and the interscale fusion to yield the final change-detection mask. For conducting the performance evaluation of change detection, the proposed DT-CWT-based unsupervised change-detection method is exploited for both the noise-free and the noisy images. Extensive simulation results clearly show that the proposed algorithm not only consistently provides more accurate detection of small changes but also demonstrates attractive robustness against noise interference under various noise types and noise levels. © 2009 IEEE.
Source Title: IEEE Transactions on Geoscience and Remote Sensing
URI: http://scholarbank.nus.edu.sg/handle/10635/77337
ISSN: 01962892
DOI: 10.1109/TGRS.2009.2029095
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

76
checked on Mar 6, 2018

WEB OF SCIENCETM
Citations

62
checked on Mar 6, 2018

Page view(s)

30
checked on Feb 25, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.