scholarly journals SAR Image Segmentation Using Region Smoothing and Label Correction

2020 ◽  
Vol 12 (5) ◽  
pp. 803
Author(s):  
Ronghua Shang ◽  
Junkai Lin ◽  
Licheng Jiao ◽  
Yangyang Li
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Majority Voting ◽  
Sar Image ◽  
Application Process ◽  
Sar Images ◽  
Sar Image Segmentation ◽  
Segmentation Methods ◽  
The Difference ◽  
Homogeneous Regions

The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.

scholarly journals REGIONAL SAR IMAGE SEGMENTATION BASED ON FUZZY CLUSTERING WITH GAMMA MIXTURE MODEL

2017 ◽  
Vol IV-2/W4 ◽  
pp. 471-475
Author(s):  
X. L. Li ◽  
Q. H. Zhao ◽  
Y. Li
Keyword(s):  
Image Segmentation ◽  
Mixture Model ◽  
Fuzzy Clustering ◽  
Voronoi Tessellation ◽  
Dissimilarity Measure ◽  
Model Parameters ◽  
Sar Image ◽  
Sar Images ◽  
Image Domain ◽  
Sar Image Segmentation

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Segmentation of Marine Spill Oil SAR Image Based on Gabor, Krawtchouk Moments and KFCM

2013 ◽  
Vol 760-762 ◽  
pp. 1462-1466 ◽  
Author(s):  
Li Zhu ◽  
Yi Quan Wu ◽  
Jun Yin
Keyword(s):  
Image Segmentation ◽  
Texture Features ◽  
Sar Image ◽  
Segmentation Method ◽  
Threshold Method ◽  
Krawtchouk Moments ◽  
Model Method ◽  
Sar Image Segmentation ◽  
Segmentation Methods ◽  
Cv Model

To further improve the accuracy of SAR image segmentation in the marine spill oil detection, a segmentation method of marine spill oil images based on Gabor, Krawtchouk moments and KFCM is proposed in this paper. Firstly, the marine spill oil image is decomposed by Gabor transform to obtain the texture features of image. Then, the Krawtchouk moments are applied to extract the shape features of image. Finally, the image segmentation is achieved based on KFCM. A large number of experimental results show that, compared with the related segmentation methods such as Tsallis entropy threshold method,CV model method and the method based on Gabor, Krawtchouk moments and FCM, the proposed method can achieve better result.

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.

Hierarchical fusion convolutional neural networks for SAR image segmentation

2021 ◽  
Vol 147 ◽  
pp. 115-123
Author(s):  
Yinyin Jiang ◽  
Ming Li ◽  
Peng Zhang ◽  
Xiaofeng Tan ◽  
Wanying Song
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Sar Image ◽  
Sar Image Segmentation

Optimal approach to SAR image segmentation and classification

2000 ◽  
Vol 147 (3) ◽  
pp. 134 ◽  
Author(s):  
D. Stewart ◽  
D. Blacknell ◽  
A. Blake ◽  
R. Cook ◽  
C. Oliver
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Sar Image Segmentation ◽  
Optimal Approach
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