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 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 SAR Image Segmentation Using Region Smoothing and Label Correction

2020 ◽  
Vol 12 (5) ◽  
pp. 803
Author(s):  
Ronghua Shang ◽  
Junkai Lin ◽  
Licheng Jiao ◽  
Yangyang Li
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Majority Voting ◽  
Sar Image ◽  
Application Process ◽  
Sar Images ◽  
Sar Image Segmentation ◽  
Segmentation Methods ◽  
The Difference ◽  
Homogeneous Regions

The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.

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.

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