UNSUPERVISED CHANGE DETECTION WITH VERY HIGH-RESOLUTION SAR IMAGES BY MULTISCALE ANALYSIS AND MARKOV RANDOM FIELDS

被引：3
|
作者：
Moser, Gabriele [1 ]
Serpico, Sebastiano B. [1 ]
机构：
[1] Univ Genoa, Dept Biophys & Elect Eng DIBE, I-16145 Genoa, Italy
来源：
2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2010年
关键词：
Unsupervised change detection; very-high resolution synthetic aperture radar; wavelets; Markov random fields; generalized Gaussian distribution;
D O I：
10.1109/IGARSS.2010.5652435
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
Change detection represents an important tool in environmental monitoring and disaster management. Here, a novel unsupervised change-detection method is proposed for very high-resolution SAR images, by integrating wavelet multi-scale feature extraction, Markov random fields for contextual modeling, and generalized Gaussian models. Experiments with COSMO-SkyMed data remark the effectiveness of the method as compared with previous methods.
引用
收藏
页码：3082 / 3085
页数：4
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