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2009 International Asia Conference on Informatics in Control, Automation and Robotics
A New Method of SAR Image Reconstruction and Segmentation
February 01-February 02
ISBN: 978-0-7695-3519-7
| ASCII Text | x | ||
| Yingying Kong, Jianjiang Zhou, "A New Method of SAR Image Reconstruction and Segmentation," Informatics in Control, Automation and Robotics, International Asia Conference on, pp. 249-253, 2009 International Asia Conference on Informatics in Control, Automation and Robotics, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/CAR.2009.45, author = {Yingying Kong and Jianjiang Zhou}, title = {A New Method of SAR Image Reconstruction and Segmentation}, journal ={Informatics in Control, Automation and Robotics, International Asia Conference on}, volume = {0}, year = {2009}, isbn = {978-0-7695-3519-7}, pages = {249-253}, doi = {http://doi.ieeecomputersociety.org/10.1109/CAR.2009.45}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Informatics in Control, Automation and Robotics, International Asia Conference on TI - A New Method of SAR Image Reconstruction and Segmentation SN - 978-0-7695-3519-7 SP249 EP253 A1 - Yingying Kong, A1 - Jianjiang Zhou, PY - 2009 KW - Markov Random Field (MRF) KW - Gamma Distribution KW - SAR image recovery KW - SAR Image Segmentation KW - Theory of Connectivity VL - 0 JA - Informatics in Control, Automation and Robotics, International Asia Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CAR.2009.45
This paper proposes the use of the inherent characteristics of SAR images to improve Gibbs-MRF model for recovering SAR image. Further, it puts forward to segment SAR image into target and shadow with the theory of connectivity in digital morphology. The new method is not only using GAMMA distribution to replace the traditional Rayleigh distribution in the estimate of MAP (Maximum A Posteriori Probability, MAP), but also using the connectivity model of pixels intensity value relevance to extract goal better in the neighborhood of SAR image pixel space. This method takes full advantage of the relevance between the information of digital morphology of the SAR image and the pixel intense, and eliminates isolated points and obtains good segment results.
Index Terms:
Markov Random Field (MRF), Gamma Distribution, SAR image recovery, SAR Image Segmentation, Theory of Connectivity
Citation:
Yingying Kong, Jianjiang Zhou, "A New Method of SAR Image Reconstruction and Segmentation," car, pp.249-253, 2009 International Asia Conference on Informatics in Control, Automation and Robotics, 2009
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