Multi-objective evolutionary for synthetic aperture radar image segmentation with non-local means denoising

2013 ◽  
Vol 13 (1) ◽  
pp. 39-53 ◽  
Author(s):  
Yangyang Li ◽  
Ying Wei ◽  
Yang Wang ◽  
Licheng Jiao
Keyword(s):  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Image ◽  
Radar Image ◽  
Synthetic Aperture ◽  
Multi Objective ◽  
Local Means ◽  
Non Local ◽  
Aperture Radar

Unsupervised Synthetic Aperture Radar Image Segmentation Using Fisher Distributions

2009 ◽  
Vol 47 (8) ◽  
pp. 2966-2972 ◽  
Author(s):  
F. Galland ◽  
J.-M. Nicolas ◽  
H. Sportouche ◽  
M. Roche ◽  
F. Tupin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Image ◽  
Radar Image ◽  
Synthetic Aperture ◽  
Aperture Radar

Synthetic aperture radar image segmentation using supervised artificial neural network

2020 ◽  
Vol 16 (4) ◽  
pp. 397-408
Author(s):  
R. Lalchhanhima ◽  
Goutam Saha ◽  
Morrel V.L. Nunsanga ◽  
Debdatta Kandar
Keyword(s):  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Radar Image ◽  
Synthetic Aperture ◽  
Intensity Information ◽  
Ground Truth Image ◽  
Aperture Radar

Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore, the segmentation process can not directly rely on the intensity information alone, but it must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use supervised information about regions for segmentation criteria in which ANN is employed to give training on the basis of known ground truth image derived. Three different features are employed for segmentation, first feature is the original image, second feature is the roughness information and the third feature is the filtered image. The segmentation accuracy is measured against the Difficulty of Segmentation (DoS) and Cross Region Fitting (CRF) methods. The performance of our algorithm has been compared with other proposed methods employing the same set of data.

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