Paper
29 December 2008 A new approach to texture segmentation of remote sensing image based on MRF and particle swarm optimization algorithm
Huazhong Jin, Zequn Guan, Benlin Xiao, Bubin Zhang
Author Affiliations +
Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72851K (2008) https://doi.org/10.1117/12.815864
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China
Abstract
In this paper, a new texture segmentation approach based on Markov random field (MRF) and global optimal method of particle swarm optimization (PSO) is proposed. According to this approach, firstly the MRF texture model is established, and potential function of Gibbs distribution and the calculating method of Gibbs parameters are represented. Then the fitness function is designed and the PSO is adopted here to solve the maximum a posterior (MAP) estimate. Finally, a comparison of the new algorithm with the Metropolis algorithm and the Gibbs Sampler is made in texture segmentation of remote sensing images. Results show that PSO algorithm can reduce the computational complexity and is much more efficient.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Huazhong Jin, Zequn Guan, Benlin Xiao, and Bubin Zhang "A new approach to texture segmentation of remote sensing image based on MRF and particle swarm optimization algorithm", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72851K (29 December 2008); https://doi.org/10.1117/12.815864
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KEYWORDS
Image segmentation

Particle swarm optimization

Particles

Image processing algorithms and systems

Evolutionary algorithms

Remote sensing

Image processing

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