Research On Image Segmentation Algorithm Based On Markov Random Field Model

The image segmentation algorithm based on Markov random field model introduces mathematical statistics theory and estimation theory into image segmentation.It effectively introduces the prior information of the image through Bayesian theory and fully considers the relationship between adjacent pixels in the image.The spatial interrelationship has fewer model parameters and has the advantage of being easy to expand.Therefore,this method has become a research hotspot in the field of image segmentation.In this paper,GF-4 remote sensing satellite image is taken as the research object.In view of the large amount of data and many bands in this image,we choose to introduce the Markov random field-based image segmentation algorithm into the remote sensing image segmentation.K-means segmentation,a commonly used image segmentation algorithm at home and abroad,is used for comparative analysis.In image segmentation,conditional iterative algorithms are used to speed up the segmentation because the satellite image data is huge.At the same time,in order to better explain the advantages of the image segmentation algorithm based on Markov random field model,an experimental data of Markov random field was constructed using MeteropolisHastiongs algorithm.By comparing with the K-means segmentation algorithm,the experimental results are analyzed.The results show that the image segmentation using Markov random field is more accurate than the segmentation using K-means,and the segmentation effect on the edges is more prominent.