Segmentation of SAR images

El-Zaart, Ali; Ziou, Djemel; Wang, Shengrui; Jiang, Qingshan

doi:10.1016/s0031-3203(01)00070-x

Cited by 89 publications

(41 citation statements)

References 13 publications

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“…On the other hand, Gamma distribution is more general to represent the both symmetric and non-symmetric modes. The Gamma function defines as [5]:…”

Section: Gamma Distributionmentioning

confidence: 99%

“…We assume that the histogram of an image can be seen as a combination of Gamma distributions. The mean values of each mode can be estimated using Gamma distribution [5]. We used Gamma distribution because it has the ability to represent both symmetric and non-symmetric mode rather than the limited Gaussian distribution that describes only the symmetric mode better.…”

Section: Bimodal Thresholdingmentioning

confidence: 99%

“…This work focuses on improving Otsu's thresholding method to generate threshold value automatically for a grayscale images based on Gamma distribution. Symmetric and asymmetric histogram distribution of the intensity values can be represented by using Gamma distribution [5]. Gamma distribution is extensively more than Gaussian distribution, which only represents symmetric histogram distribution of the intensity values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MRI Images Thresholding for Alzheimer Detection

El-Zaart¹,

Ghosn²

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…On the other hand, Gamma distribution is more general to represent the both symmetric and non-symmetric modes. The Gamma function defines as [5]:…”

Section: Gamma Distributionmentioning

confidence: 99%

Section: Bimodal Thresholdingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MRI Images Thresholding for Alzheimer Detection

El-Zaart¹,

Ghosn²

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…It has received an increasing amount of attention and therefore hundreds of approaches have been proposed over the last few decades [1]. At present, SAR images have been widely used in hydrology, remote sensing, military, and other fields, to obtain accurate information of remote sensing image which is the key for better application.…”

Section: Introductionmentioning

confidence: 99%

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

et al. 2018

Mathematical Problems in Engineering

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An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

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“…Researchers have proposed many kinds of segmentation algorithms for SAR images, which include threshold methods, 3,[17][18][19] spectral clustering (SC) algorithms, 20,21 statistic model-based methods, 1,14,15,[22][23][24][25] artificial intelligence methods, [26][27][28][29][30] support vector machine (SVM), 6,31 region growing methods, 15,[32][33][34][35] and so on. Among these algorithms, cluster-based algorithms form one popular and representative family, whose main idea is to group pixels in such a way that the pixels in the same group are more similar to each other than those in other groups.…”

Section: Introductionmentioning

confidence: 99%

Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

Feng

2015

J. Appl. Remote Sens

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Abstract. Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Segmentation of SAR images

Cited by 89 publications

References 13 publications

MRI Images Thresholding for Alzheimer Detection

MRI Images Thresholding for Alzheimer Detection

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

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