scholarly journals SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction

Sensors ◽  
2013 ◽  
Vol 13 (11) ◽  
pp. 14484-14499 ◽  
Author(s):  
Quanhua Zhao ◽  
Yu Li ◽  
Zhenggang Liu
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Bayesian Inference ◽  
Voronoi Tessellation ◽  
Dark Spot ◽  
Sar Image ◽  
Sar Image Segmentation

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.

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

SAR Image Segmentation Based on Maximum Variance Method and Morphology

2013 ◽  
Vol 798-799 ◽  
pp. 761-764
Author(s):  
Ming Xia Xiao
Keyword(s):  
Image Segmentation ◽  
Noise Suppression ◽  
Operation Time ◽  
Differential Method ◽  
Good Effect ◽  
Sar Image ◽  
Variance Method ◽  
Maximum Variance ◽  
Sar Image Segmentation ◽  
A New Technique

A new technique that combines maximum variance method and morphology was presented for Synthetic Aperture Radar (SAR) image segmentation in target detection. Firstly, using the first-order differential method to enhance the original image for highlighting edge details of the image; then using the maximum variance method to calculate the gray threshold and segment the image; lastly, the mathematical morphology was used to processing the segmented image, which could prominently improve the segmentation effects. Experiments show that this algorithm can obtain accurate segmentation results, and have a good effect on noise suppression, edge detail protection and operation time.

Fast Pixel-Superpixel Region Merging for SAR Image Segmentation

2020 ◽  
pp. 1-17
Author(s):  
Deliang Xiang ◽  
Fan Zhang ◽  
Wei Zhang ◽  
Tao Tang ◽  
Dongdong Guan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Region Merging ◽  
Sar Image Segmentation
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