A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images

被引:67
作者
Celik, Turgay [1 ,2 ]
机构
[1] ASTAR, Bioinformat Inst, Singapore, Singapore
[2] Natl Univ Singapore, Fac Sci, Singapore 117548, Singapore
关键词
Unsupervised change detection; Bayesian inferencing; SAR image analysis; Dual-tree complex wavelet transform; Multiscale analysis; MULTITEMPORAL SAR IMAGES; ALGORITHM; MISREGISTRATION;
D O I
10.1016/j.sigpro.2009.10.018
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, an unsupervised change detection technique for synthetic aperture radar (SAR) images acquired on the same geographical area but at different time instances is proposed by conducting probabilistic Bayesian inferencing with expectation maximization-based parameter estimation to perform unsupervised 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, which is obtained by taking the logarithm of the pixel ratio of two SAR images, aimed at achieving different scales of representation of the change signal. Intra- and inter-scale data fusion is performed to enhance the change detection performance. Experimental results obtained on SAR images acquired by the ERS-1, and JERS satellites confirm the effectiveness of the proposed approach. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:1471 / 1485
页数:15
相关论文
共 18 条
[1]   An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images [J].
Bazi, Y ;
Bruzzone, L ;
Melgani, F .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (04) :874-887
[2]   A novel method for mapping land cover changes: Incorporating time and space with geostatistics [J].
Boucher, Alexandre ;
Seto, Karen C. ;
Journel, Andre G. .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (11) :3427-3435
[3]   A detail-preserving scale-driven approach to change detection in multitemporal SAR images [J].
Bovolo, F ;
Bruzzone, L .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (12) :2963-2972
[4]   Automatic analysis of the difference image for unsupervised change detection [J].
Bruzzone, L ;
Prieto, DF .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2000, 38 (03) :1171-1182
[5]   An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images [J].
Bruzzone, L ;
Serpico, SB .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1997, 35 (04) :858-867
[6]   Digital change detection methods in ecosystem monitoring: a review [J].
Coppin, P ;
Jonckheere, I ;
Nackaerts, K ;
Muys, B ;
Lambin, E .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2004, 25 (09) :1565-1596
[7]   The effects of image misregistration on the accuracy of remotely sensed change detection [J].
Dai, XL ;
Khorram, S .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1998, 36 (05) :1566-1577
[8]  
DERRODE S, 2003, MULTITEMP
[9]   Change detection of multitemporal SAR data in urban areas combining feature-based and pixel-based techniques [J].
Gamba, Paolo ;
Dell'Acqua, Fabio ;
Lisini, Gianni .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (10) :2820-2827
[10]   An efficient k-means clustering algorithm:: Analysis and implementation [J].
Kanungo, T ;
Mount, DM ;
Netanyahu, NS ;
Piatko, CD ;
Silverman, R ;
Wu, AY .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2002, 24 (07) :881-892