scholarly journals SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
M. Q. Li ◽  
L. P. Xu ◽  
Na Xu ◽  
Tao Huang ◽  
Bo Yan
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
State Of The Art ◽  
Sar Image ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Sar Image Segmentation ◽  
Segmentation Approach

An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation

2013 ◽  
Vol 93 (2) ◽  
pp. 487-499 ◽  
Author(s):  
Jie Feng ◽  
L.C. Jiao ◽  
Xiangrong Zhang ◽  
Maoguo Gong ◽  
Tao Sun
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Edge Preservation ◽  
Fuzzy C Means ◽  
Sar Image Segmentation ◽  
Non Local ◽  
Fuzzy C Means Algorithm

scholarly journals COUPLING REGULAR TESSELLATION WITH RJMCMC ALGORITHM TO SEGMENT SAR IMAGE WITH UNKNOWN NUMBER OF CLASSES

2016 ◽  
Vol XLI-B7 ◽  
pp. 393-397 ◽  
Author(s):  
Y. Wang ◽  
Y. Li ◽  
Q. H. Zhao
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Image Domain ◽  
Unknown Number ◽  
Bayesian Paradigm ◽  
Sar Image Segmentation ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Homogeneous Regions ◽  
Number Of Classes

This paper presents a Synthetic Aperture Radar (SAR) image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC') algorithm. First of all, an image domain is portioned into a set of blocks by regular tessellation. The image is modeled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent Gamma distribution. By Bayesian paradigm, the posterior distribution is obtained to build the region-based image segmentation model. Then, a RJMCMC algorithm is designed to simulate from the segmentation model to determine the number of homogeneous regions and segment the image. In order to further improve the segmentation accuracy, a refined operation is performed. To illustrate the feasibility and effectiveness of the proposed approach, two real SAR image is tested.

scholarly journals COUPLING REGULAR TESSELLATION WITH RJMCMC ALGORITHM TO SEGMENT SAR IMAGE WITH UNKNOWN NUMBER OF CLASSES

2016 ◽  
Vol XLI-B7 ◽  
pp. 393-397
Author(s):  
Y. Wang ◽  
Y. Li ◽  
Q. H. Zhao
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Image Domain ◽  
Unknown Number ◽  
Bayesian Paradigm ◽  
Sar Image Segmentation ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Homogeneous Regions ◽  
Number Of Classes

This paper presents a Synthetic Aperture Radar (SAR) image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC') algorithm. First of all, an image domain is portioned into a set of blocks by regular tessellation. The image is modeled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent Gamma distribution. By Bayesian paradigm, the posterior distribution is obtained to build the region-based image segmentation model. Then, a RJMCMC algorithm is designed to simulate from the segmentation model to determine the number of homogeneous regions and segment the image. In order to further improve the segmentation accuracy, a refined operation is performed. To illustrate the feasibility and effectiveness of the proposed approach, two real SAR image is tested.

Sign in / Sign up

Export Citation Format

Share Document