Abstract
A new unsupervised segmentation algorithm of SAR(Synthetic aperture radar) imageries based on multiscale Stochastic Models is proposed, considering non-gaussian statistical property of SAR image data and Markov property of neighboring scales. Since EM(expectation maximum) algorithm can not get the parameter estimation of non-gauss distribution, MAR(Multiscale Autoregressive) model is used for extracting image Feature data which obeys gauss distribution. HMT(Hidden Markov Tree) model can be used to model image consisting of multi-scale feature data, which can be approximated by mixed gauss distribution and its parameters can be straightly trained by EM algorithm. Then we propose a context model to fuse feature information of multiscale. Finally, we obtain a new unsupervised segmentation approach for SAR imageries. Simulations on SAR imagery indicate that the new approach improves segmentation accuracy in some degree.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
