Synthetic aperture radar (SAR) image segmentation using a new modified fuzzy c-means algorithm

Chumsamrong, W.; Thitimajshima, Punya; Rangsanseri, Yuttapong

doi:10.1109/igarss.2000.861651

Cited by 24 publications

(17 citation statements)

References 6 publications

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“…In addition, when compared with [19], [20], this method proved its efficiency for the high amount of embedded data and a better robustness against different geometrical attacks As shown in Fig. 9, our proposed algorithm is highly more robust to JPEG compression when compared with different well known algorithms in the DCT and spatial domains such as Kutter, Cox, Koch, Langelaar, Bruyndonckx, and Frifirich algorithms [21], [22], [24]- [26]. …”

Section: ) Jpeg Compression Attackmentioning

confidence: 94%

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Multiple Binary Images Watermarking in Spatial and Frequency Domains

Oueslati¹,

Chérif²,

Solaiman³

2013

IJCTE

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Abstract-Watermarking is used for the protection of intellectual property, data integrity, and data authentication. This paper proposes a novel method for image watermarking based on embedding multiples watermarks in different domains of the image representation (spatial and DCT domains), without any distortion of the watermarked image. In the spatial domain, the processing method is based on study of segmentation by fuzzy c-means clustering method that outputs the zones of watermark embedding and respectively the associated appropriate embedding gain factors. However in the DCT domain a proper choice of the DCT coefficients based on the quantization JPEG table in the middle frequencies band is carried out. Several watermarks were embedded in these two domains in order to take advantage of the spatial domain robustness against different asynchronous attacks, associated to the DCT domain robustness against jpeg compression and some other signal processing distortions. Experimental results show that the proposed method is robust against a large set of synchronous and asynchronous image attacks such as filtering, lossy compression, cropping and rotation attack. IndexTerms-Image watermarking, multi-domains, multi-watermark, insertion force, fuzzy c-mean (FCM)

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Section: ) Jpeg Compression Attackmentioning

confidence: 94%

“…FCM is an unsupervised clustering technique which has been utilized in a wide variety of image processing applications such as medical imaging [16] and remote sensing [17]. In fact, an image can be represented in terms of pixels, which are associated with a location and a gray level value.…”

Section: Determination Zones Of Insertion By Methods Fuzzy C-meansmentioning

confidence: 99%

Multiple Binary Images Watermarking in Spatial and Frequency Domains

Oueslati¹,

Chérif²,

Solaiman³

2013

IJCTE

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“…Fuzzy c-mean, proposed by Bezdek [47], is one of the main techniques of unsupervised machine learning algorithm which is widely applied to the image segmentation [48]. Fuzzy clustering has been proved to be very well suited to deal with the imprecise nature of geographical information including remote sensing data [49].…”

Section: The Adaptive Fcm Algorithmmentioning

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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“…These models include Markov random field and Gibbs random field (Dong et al 1999(Dong et al , 2001, neural networks (Liu et al 2001), and fuzzy logic methods (Chumsamrong et al 2000), etc. The Gaussian Markov random field (GMRF) models have been shown to be effective in dealing with images with a high level of noise, such as synthetic aperture radar (SAR) images (Dong et al 2001).…”

Section: Introductionmentioning

confidence: 99%

An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery

Gong

2005

International Journal of Remote Sensing

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Texture analysis of remote sensing images based on classification of area units represented in image segments is usually more accurate than operating on an individual pixel basis. In this paper we suggest a two-step procedure to segment texture patterns in remotely sensed data. An image is first classified based on texture analysis using a multi-parameter and multi-scale technique. The intermediate results are then treated as initial segments for subsequent segmentation based on the Gaussian Markov random field (GMRF) model. The segmentation procedure seeks to merge pairs of segments with the minimum variance difference. Experiments using real data prove that the two-step procedure improves both computational efficiency and accuracy of texture classification.

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Synthetic aperture radar (SAR) image segmentation using a new modified fuzzy c-means algorithm

Cited by 24 publications

References 6 publications

Multiple Binary Images Watermarking in Spatial and Frequency Domains

Multiple Binary Images Watermarking in Spatial and Frequency Domains

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

An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery

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