A Hierarchical Student’s t-Distributions Based Unsupervised SAR Image Segmentation Method

2019 ◽  
pp. 472-483
Author(s):  
Yuhui Zheng ◽  
Yahui Sun ◽  
Le Sun ◽  
Hui Zhang ◽  
Byeungwoo Jeon
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Segmentation Method ◽  
Sar Image Segmentation ◽  
Student’S T

scholarly journals An Energy-Based SAR Image Segmentation Method with Weighted Feature

2019 ◽  
Vol 11 (10) ◽  
pp. 1169 ◽  
Author(s):  
Yu Wang ◽  
Guoqing Zhou ◽  
Haotian You
Keyword(s):  
Image Segmentation ◽  
Spectral Feature ◽  
Curvelet Transform ◽  
Structural Features ◽  
Image Texture ◽  
Sar Image ◽  
Segmentation Method ◽  
Multiple Features ◽  
Image Domain ◽  
Sar Image Segmentation

To extract more structural features, which can contribute to segment a synthetic aperture radar (SAR) image accurately, and explore their roles in the segmentation procedure, this paper presents an energy-based SAR image segmentation method with weighted features. To precisely segment a SAR image, multiple structural features are incorporated into a block- and energy-based segmentation model in weighted way. In this paper, the multiple features of a pixel, involving spectral feature obtained from original SAR image, texture and boundary features extracted by a curvelet transform, form a feature vector. All the pixels’ feature vectors form a feature set of a SAR image. To automatically determine the roles of the multiple features in the segmentation procedure, weight variables are assigned to them. All the weight variables form a weight set. Then the image domain is partitioned into a set of blocks by regular tessellation. Afterwards, an energy function and a non-constrained Gibbs probability distribution are used to combine the feature and weight sets to build a block-based energy segmentation model with feature weighted on the partitioned image domain. Further, a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model. In the RJMCMC algorithm, three move types were designed according to the segmentation model. Finally, the proposed method was tested on the SAR images, and the quantitative and qualitative results demonstrated its effectiveness.

A Bayesian Decision Method with Minimal Error Rate for SAR Image Segmentation

2011 ◽  
Vol 65 ◽  
pp. 509-513
Author(s):  
Li Jun Tian ◽  
Da Hui Li
Keyword(s):  
Image Segmentation ◽  
Error Rate ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Bayesian Decision ◽  
Segmentation Method ◽  
Feasible Method ◽  
Decision Method ◽  
Minimal Error ◽  
Sar Image Segmentation

The paper shows a segmentation method of Synthetic Aperture Radar (SAR) image. In the method, firstly estimate the different parameters with normal distribution from histogram. Then make different judgment on each pixel. Finally make experiments in many images and the image segmentation results show that the method can reduce noise; it is a feasible method for SAR image segmentation.

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.

Unsupervised SAR image segmentation method based on MAP classification criterion and anisotropic diffusion smoothing

2009 ◽  
Author(s):  
Shu Run Tan ◽  
Xin Yi He ◽  
Bo Zhao ◽  
Xiao Yang Zhou ◽  
Zhong Jin Jiang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Anisotropic Diffusion ◽  
Sar Image ◽  
Classification Criterion ◽  
Segmentation Method ◽  
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.

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