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.

scholarly journals AN ENERGY SEGMENTATION METHOD OF HIGH-RESOLUTION SAR IMAGE BASED ON MULTIPLE FEATURES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 303-307
Author(s):  
Y. Wang ◽  
H. T. You ◽  
T L. Liu
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Energy Function ◽  
Feature Vector ◽  
Curvelet Transform ◽  
Sar Image ◽  
Segmentation Method ◽  
Multiple Features ◽  
Global Energy ◽  
Image Domain

Abstract. To achieve the optimal image segmentation, an energy segmentation method based on multiple features and blocks for high resolution Synthetic Aperture Radar (SAR) image is proposed in this paper. First of all, a feature vector of pixel is formed with the texture feature extracted by curvelet transform and means function, the boundary feature extracted by curvelet transform and Canny Operator, and the original spectral feature; a feature set is formed by all feature vectors of pixels in the image. The feature vector is considered as segmentation basis, and its domain is partitioned by regular tessellation. On the partitioned image domain, a label variable is assigned to a regular block; each homogeneous region is fitted by one or more regular blocks; Obviously, a label field is formed by all the label variables of regular blocks. The model of label field is built by using energy function of neighborhood relationship. The feature set is considered as a realization of a random filed of multiple features (multiple features field for short). A heterogeneous energy function is used to establish the model of multiple features field. Then the established models of the label field and multiple features field are combined to define global energy function of image segmentation, and non-constrained Gibbs probability distribution is used to describe the global energy function to build the energy segmentation model based on multiple features. Further, a RJMCMC algorithm is designed to simulate from the model to segment SAR image. To verify the feasibility and superiority of the proposed approach, real SAR images are tested.

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.

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.

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