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

页码：3082 / 3085

页数：4

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