Unsupervised change detection with very high-resolution SAR images by multiscale analysis and Markov random fields

Author

Moser, Gabriele ; Serpico, Sebastiano B.

Author_Institution

Dept. of Biophys. & Electron. Eng., Univ. of Genoa, Genoa, Italy

fYear

2010

fDate

25-30 July 2010

Firstpage

3082

Lastpage

3085

Abstract

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 multiscale 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.

Keywords

Gaussian processes; Markov processes; environmental factors; feature extraction; monitoring; radar imaging; synthetic aperture radar; COSMO-SkyMed; Markov random fields; disaster management; environmental monitoring; generalized Gaussian models; high-resolution SAR images; multiscale analysis; multiscale feature extraction; unsupervised change detection; Accuracy; Discrete wavelet transforms; Ice; Markov processes; Pixel; Remote sensing; Speckle; Markov random fields; Unsupervised change detection; generalized Gaussian distribution; very-high resolution synthetic aperture radar; wavelets;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International

Conference_Location

Honolulu, HI

ISSN

2153-6996

Print_ISBN

978-1-4244-9565-8

Electronic_ISBN

2153-6996

Type

conf

DOI

10.1109/IGARSS.2010.5652435

Filename

5652435