Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/77502
Title: Unsupervised multiscale change detection in multitemporal synthetic aperture radar images
Authors: Celik, T. 
Issue Date: 2009
Source: Celik, T. (2009). Unsupervised multiscale change detection in multitemporal synthetic aperture radar images. European Signal Processing Conference : 1547-1551. ScholarBank@NUS Repository.
Abstract: In this paper, an unsupervised change detection technique for synthetic aperture radar (SAR) images is proposed by conducting probabilistic Bayesian inferencing with expectation maximization-based parameter estimation to perform unsu-pervised thresholding over the data collected from the dual-tree complex wavelet transform (DT-CWT) subbands generated at the various scales. The proposed approach exploits a DT-CWT-based multiscale decomposition of the log-ratio image aimed at achieving different scales of representation of the change signal. Experimental results obtained on multitemporal SAR images acquired by the ERS-1, and JERS satellites confirm the effectiveness of the proposed approach. © EURASIP, 2009.
Source Title: European Signal Processing Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/77502
ISSN: 22195491
Appears in Collections:Staff Publications

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