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Unsupervised SAR Image Segmentation Based on Multi-features
基于多特征的SAR图像的无监督分割

WANG Qing-xiang,LI Di,ZHANG Wu-jie,
王庆香
,李迪,张舞杰

计算机科学 , 2010,
Abstract: For synthetic aperture radar(SAR) images with the characteristics of complex texture,large brightness range and vague bridge boundary,a method of unsupervised SAR image segmentation based on multi-features was presented.First of all,fcatures of the local moments and the statistics(contrast,correlation,entropy,homogcneity) of gray level cooccurrence matrix were extracted. Secondly,the dimensional reduction operation by principal component analysis(PCA)was applied to these extracted features in order to obtain 2-dimensional features with adequate category information. Finally,pixels with 2-D feature information were automatically clustered by the Mean Shift method. As the Mean Shift clustering method needn't provide the number of cluster, this processing is an unsupervised process of automatic segmentation. Composite image with 13rodatz textures and SAR images were tested in segmenting experiments and the re- sups demonstrate the method can achieve more accurate segmentation than other two methods in which only the gray level co-occurrence matrix or moments are employed.
Clustering-based target fast detection for SAR imagery
基于聚类的SAR图像快速目标检测

PAN Zhuo,GAO Xin,WANG Yan-fei,Fan Li-jie,
潘卓
,高鑫,王岩飞,范俐捷

计算机应用研究 , 2008,
Abstract: To solve the inefficient and high false alarm probability problem of the target detection in SAR images,this paper proposed a fast target-detection scheme for SAR images,which combined improved Mean Shift clustering and two-parameter CFAR detection technique.According to the SAR image character,the Mean Shift clustering algorithm was improved.Clustering SAR images for preprocessing,which reduced the effect of clutter and eliminated many false target detections from background.Furthermore,performed an initial clustering incorporates the concept of image structure into the target detection process,which the pixel-by-pixel detection was avoided.Numerical experiments show that the novel method performs better accurately and faster speed.
Automatic Segmentation for Synthetic Aperture Radar Images
SAR图像的自动分割方法研究

Li Ying,Shi Qing-feng,Zhang Yan-ning,Zhao Rong-chun,
李映
,史勤峰,张艳宁,赵荣椿

电子与信息学报 , 2006,
Abstract: The multiplicative nature of the speckle noise in SAR images is a big problem in SAR image segmentation. A novel method for automatic segmentation of SAR images is proposed. The wavelet energy is used to extract texture features, the regional statistics is used to extract gray-level features and the edge preserving mean of gray-level features is used to ensure the accuracy of classification of pixels near to the edge. Three representative kinds of features of SAR image are extracted, so the segmentation performance is enhanced. Besides, an improved unsupervised clustering algorithm is proposed for image segmentation, which can determine the number of classes automatically. Segmentation results on real SAR image demonstrate the effectiveness of the proposed method.
SAR image change detection based on Memetic algorithm
基于Memetic算法的SAR图像变化检测

XIN Fang-Fang,JIAO Li-Cheng,WANG Gui-Ting,
辛芳芳
,焦李成,王桂婷

红外与毫米波学报 , 2012,
Abstract: This paper proposed an unsupervised technique for detecting changed areas between multitemporal SAR images. Different with the original ones, the clustering method was used here to find the change map by minimizing mean square error with evolution algorithm. After introducing the image character, a new search strategy in Memetic algorithm was given here, which adjusted the local search algorithm according to the current detection result. The approach was distribution free and did not need priori knowledge. The experimental results obtained on the real SAR images showed that the proposed method had a higher convergence speed than GA,ICSA and original MA, the detection results demonstrated the effectiveness of the proposed algorithm.
Vectorial total variation model for multi-channel SAR image denoising
多通道SAR图像滤波的向量总变分模型

Li Wen-Ping,Wang Zheng-Ming,Xie Mei-Hua,
李文屏
,王正明,谢美华

红外与毫米波学报 , 2012,
Abstract: This paper proposed an unsupervised technique for detecting changed areas between multitemporal SAR images. Different with the original ones, the clustering method was used here to find the change map by minimizing mean square error with evolution algorithm. After introducing the image character, a new search strategy in Memetic algorithm was given here, which adjusted the local search algorithm according to the current detection result. The approach was distribution free and did not need priori knowledge. The experimental results obtained on the real SAR images showed that the proposed method had a higher convergence speed than GA,ICSA and original MA, the detection results demonstrated the effectiveness of the proposed algorithm.
Unsupervised segmentation of SAR image based on multiscale stochastic model
基于多尺度随机模型的SAR图像无监督分割

XU Hai-xia,TIAN Zheng,MENG Fan,
徐海霞
,田 铮,孟 帆

计算机应用 , 2005,
Abstract: The presence of speckle in Synthetic Aperture Radar(SAR) images makes the segmentation of such images difficult,either by gray levels or by texture.According to the mechanism of SAR imaging,two unsupervised segmentation methods were proposed based on two class of multiscale stochastic model,namely multiscale autoregressive(MAR) model and multiscale autoregressive moving average(MARMA) model.These models capture the statistical information in a multiscale sequence of SAR image,which is then used to implement unsupervised segmentation of SAR image via multiresolution mixture algorithm.Experimental results over SAR images confirm the proposed segmentation methods are valid.
Data-Driven Polinsar Unsupervised Classification Based on Adaptive Model-Based Decomposition and Shannon Entropy Characterization
Hui Song;Wen Yang;Xin Xu;Mingsheng Liao
PIER B , 2013, DOI: 10.2528/PIERB13012302
Abstract: We introduce a data-driven unsupervised classification algorithm that uses polarimetric and interferometric synthetic aperture radar (PolInSAR) data. The proposed algorithm uses a classification method that preserves scattering characteristics. Our contribution is twofold. First, the method applies adaptive model-based decomposition (AMD) to represent the scattering mechanism, which overcomes the flaws introduced by Freeman decomposition. Second, a new class initialization scheme using a histogram clustering algorithm based on a Dirichlet process mixture model is applied to automatically determine the number of clusters and effectively initialize the classes. Therefore, our algorithm is data-driven. In the first step, the Shannon entropy characteristics of the PolInSAR data are extracted and used to calculate the local histogram features. After applying AMD, pixels are divided into three canonical scattering categories according to their dominant scattering mechanism. The histogram clustering algorithm is applied to each scattering category to obtain the number of classes and initialize them. The iterative Wishart classifier is applied to refine the classification results. Our method not only can obtain promising unsupervised classification results but also can automatically assign the number of classes. Experimental results for E-SAR L-band PolInSAR images from the German Aerospace Center demonstrate the effectiveness of the proposed algorithm.
Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm  [PDF]
Xian-Bin Wen,Hua Zhang,Ze-Tao Jiang
Sensors , 2008, DOI: 10.3390/s8031704
Abstract: A valid unsupervised and multiscale segmentation of synthetic aperture radar(SAR) imagery is proposed by a combination GA-EM of the Expectation Maximization(EM) algorith with the genetic algorithm (GA). The mixture multiscale autoregressive(MMAR) model is introduced to characterize and exploit the scale-to-scale statisticalvariations and statistical variations in the same scale in SAR imagery due to radar speckle,and a segmentation method is given by combining the GA algorithm with the EMalgorithm. This algorithm is capable of selecting the number of components of the modelusing the minimum description length (MDL) criterion. Our approach benefits from theproperties of the Genetic and the EM algorithm by combination of both into a singleprocedure. The population-based stochastic search of the genetic algorithm (GA) exploresthe search space more thoroughly than the EM method. Therefore, our algorithm enablesescaping from local optimal solutions since the algorithm becomes less sensitive to itsinitialization. Some experiment results are given based on our proposed approach, andcompared to that of the EM algorithms. The experiments on the SAR images show that theGA-EM outperforms the EM method.
Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm
Xian-Bin Wen,Hua Zhang,Ze-Tao Jiang
Sensors , 2008,
Abstract: A valid unsupervised and multiscale segmentation of synthetic aperture radar(SAR) imagery is proposed by a combination GA-EM of the Expectation Maximization(EM) algorith with the genetic algorithm (GA). The mixture multiscale autoregressive(MMAR) model is introduced to characterize and exploit the scale-to-scale statisticalvariations and statistical variations in the same scale in SAR imagery due to radar speckle,and a segmentation method is given by combining the GA algorithm with the EMalgorithm. This algorithm is capable of selecting the number of components of the modelusing the minimum description length (MDL) criterion. Our approach benefits from theproperties of the Genetic and the EM algorithm by combination of both into a singleprocedure. The population-based stochastic search of the genetic algorithm (GA) exploresthe search space more thoroughly than the EM method. Therefore, our algorithm enablesescaping from local optimal solutions since the algorithm becomes less sensitive to itsinitialization. Some experiment results are given based on our proposed approach, andcompared to that of the EM algorithms. The experiments on the SAR images show that theGA-EM outperforms the EM method.
Study of Polarimetric Interferometric Unsupervised Wishart Classification of SAR Images
SAR图像的极化干涉非监督Wishart分类方法和实验研究

Yang Zhen,Yang Ru-liang,Liu Xiu-qing,
杨震
,杨汝良,刘秀清

电子与信息学报 , 2004,
Abstract: In this paper, an unsupervised Wishart classification scheme for polarimetric interferometric SAR data sets is introduced. A 6x6 polarimetric interferometric coherency matrix is used, in order to take simultaneously into account the full polarimetric information from one single image and the coherency information between the pair of these images. This classification scheme is applied to full polarimetric interferometric L-band SAR image pairs of Tien Shan, China, acquired by the NASA/JPL SIR-C/X-SAR sensor in 1994. The results show that this scheme can identify different terrain types and at the same time keep thr details of surface features, which improve greatly the results of the polarimetric unsupervised Wishart classification.
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