A Variational Level Set SAR Image Segmentation Approach Based on Statistical Model

2011 ◽  
Vol 30 (12) ◽  
pp. 2862-2866 ◽  
Author(s):  
Zong-jie Cao ◽  
Rui Min ◽  
Ling-li Pang ◽  
Yi-ming Pi
Keyword(s):  
Image Segmentation ◽  
Statistical Model ◽  
Level Set ◽  
Sar Image ◽  
Sar Image Segmentation ◽  
Segmentation Approach ◽  
Variational Level Set

A novel multi-scale level set method for SAR image segmentation based on a statistical model

2012 ◽  
Vol 33 (17) ◽  
pp. 5600-5614 ◽  
Author(s):  
Haigang Sui ◽  
Chuan Xu ◽  
Junyi Liu ◽  
Kaimin Sun ◽  
Chengfeng Wen
Keyword(s):  
Image Segmentation ◽  
Statistical Model ◽  
Level Set Method ◽  
Level Set ◽  
Sar Image ◽  
Scale Level ◽  
Multi Scale ◽  
Sar Image Segmentation

A Variational Level Set Remote Sensing SAR Image Segmentation Approach for Oil Spill Detecting Based on Fuzzy Cluster

2015 ◽  
Vol 719-720 ◽  
pp. 1049-1055 ◽  
Author(s):  
Jin Yu Liu ◽  
Zheng Ning Zhang ◽  
He Meng Yang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Oil Spill ◽  
Fuzzy Clustering ◽  
Sar Image ◽  
Segmentation Method ◽  
Level Set Function ◽  
Sar Image Segmentation ◽  
Variational Level Set ◽  
Set Function

Synthetic Aperture Radar (SAR) has become one of the important means for the ocean remote sensing detection of oil spills. The existing SAR image segmentation method has the issues of edge blur, poor contrast, non-uniform intensity image, so the segmentation effect is not ideal. This paper presents a variational level set SAR image of oil spill detection method based on fuzzy clustering. First of all, apply the threshold method on initial segmentation of the original SAR image to transform the initial segmented image as fuzzy clustering. Secondly, introduce the clustering results into the initial level set function to achieve the initial contour. Finally, add fuzzy clustering model in the level set energy function to complete the level set evolution process and get the final segmented image. This paper uses the threshold segmentation results to achieve the initialization of the variational level set function profile. In theory, it could improve the level set method for efficiency, and reduce the wrong segmentation phenomenon. The experimental results show that the SAR image segmentation method of oil spill has good segmentation qualities and is suitable for the edge complex image segmentation.

A Novel Level Set Method for Inhomogeneous SAR Image Segmentation

2020 ◽  
pp. 1-5
Author(s):  
Wenjing He ◽  
Hongjun Song ◽  
Yuanyuan Yao ◽  
Xinlin Jia ◽  
Yajun Long
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Sar Image ◽  
Sar Image Segmentation

scholarly journals Polarimetric SAR image segmentation with B-splines and a new statistical model

2010 ◽  
Vol 21 (4) ◽  
pp. 319-342 ◽  
Author(s):  
Alejandro C. Frery ◽  
Julio Jacobo-Berlles ◽  
Juliana Gambini ◽  
Marta E. Mejail
Keyword(s):  
Image Segmentation ◽  
Statistical Model ◽  
Sar Image ◽  
Polarimetric Sar ◽  
B Splines ◽  
Sar Image Segmentation

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.

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.

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