scholarly journals An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

Algorithms ◽  
2015 ◽  
Vol 8 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Dongdong Yang ◽  
Hui Yang ◽  
Rong Fei
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Kernel Space ◽  
Multi Objective ◽  
Sar Image Segmentation ◽  
Segmentation Framework

Artificial immune multi-objective SAR image segmentation with fused complementary features

2011 ◽  
Vol 181 (13) ◽  
pp. 2797-2812 ◽  
Author(s):  
Dongdong Yang ◽  
Licheng Jiao ◽  
Maoguo Gong ◽  
Fang Liu
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Artificial Immune ◽  
Multi Objective ◽  
Sar Image Segmentation

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.

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