DocumentCode
297777
Title
Markov random field based image segmentation with adaptive neighborhoods to the detection of fine structures in SAR data
Author
Smits, P.C. ; Dellepiane, S. ; Serpico, S.B.
Author_Institution
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Volume
1
fYear
1996
fDate
27-31 May 1996
Firstpage
714
Abstract
In the Markov random field (MRF) region label approach for synthetic aperture radar (SAR) image segmentation small structures may be lost. This is due to the filtering effect of the MRF region label model, which is desirable in homogeneously labelled areas like agricultural regions. End-users also interested in resource management, may wish to preserve the small structures such as small roads and rivers. To this end, the neighborhood set used in the MRF region label model has been made adaptive, based on a simple Bayesian network (BN). Results using synthetic aperture radar (SAR) data show that an important improvement of the representation of small structures is possible if they can be detected to some extent using the maximum likelihood approach
Keywords
Bayes methods; Markov processes; adaptive signal processing; geophysical signal processing; geophysical techniques; image representation; image segmentation; radar imaging; radar signal processing; remote sensing by radar; spaceborne radar; synthetic aperture radar; Bayes method; Bayesian network; Markov random field; adaptive neighborhoods; adaptive signal processing; fine structure detection; geophysical measurement technique; high resolution; image segmentation; land surface; maximum likelihood approach; radar imaging; radar remote sensing; region label approach; small structure representation; synthetic aperture radar; terrain mapping; Bayesian methods; Filtering; Image segmentation; Markov random fields; Maximum likelihood detection; Radar detection; Resource management; Rivers; Roads; Synthetic aperture radar;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 1996. IGARSS '96. 'Remote Sensing for a Sustainable Future.', International
Conference_Location
Lincoln, NE
Print_ISBN
0-7803-3068-4
Type
conf
DOI
10.1109/IGARSS.1996.516451
Filename
516451
Link To Document