Paper Info
Title
Gaussian Mixture Model Segmentation Algorithm for Remote Sensing Image
Abstract
The paper proposed a novel method for remote sensing image segmentation based on mixture model. The remote sensing image data would be considered as Gaussian mixture model. The image segmentation result was corresponding to the image label field which was a Markov Random Field(MRF). So, the image segmentation procedure was transformed to a Maximum A Posteriori(MAP) problem by Beyesian theorem. The intensity difference and the spatial distance between the two pixels in the same clique were employed in the potential function. The Iterative Conditional Model(ICM) is employed to solve MAP. In the experiments, the method is compared with the traditional MRF segmentation method using ICM and simulate annealing(SA). The experiments proved that this algorithm was more efficient than the traditional MRF one.
Year
DOI
Venue
2010
10.1109/MVHI.2010.152
MVHI
Keywords
Field
DocType
gaussian mixture model,novel method,remote sensing image,image segmentation,gaussian mixture model segmentation,markov random field,image segmentation procedure,traditional mrf,traditional mrf segmentation method,image segmentation result,image data,maximum a posteriori,guassian mixture model,mixture model,image label field,simulated annealing,weather forecasting,gaussian processes,automation,sun,maximum likelihood estimation,pixel,machine vision,iterative methods,markov processes,remote sensing
Scale-space segmentation,Markov random field,Computer science,Remote sensing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Minimum spanning tree-based segmentation,Computer vision,Pattern recognition,Image texture,Algorithm,Maximum a posteriori estimation,Mixture model,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-6596-5
0
0.34
References 
Authors
8
4
Name
Order
Citations
PageRank
Yimin Hou152.91
Xiaoli Sun202.37
Xiangmin Lun331.84
Jianjun Lan410.75