SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction

Zhao, Quanhua; Li, Yu; Liu, Zheng-gang

doi:10.3390/s131114484

Cited by 19 publications

(11 citation statements)

References 24 publications

(27 reference statements)

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“…Finally, the proposed method is tested on the SAR images to demonstrate its effectiveness. Image segmentation is a prelude for further SAR image processing tasks such as feature extraction [14], object recognition [15] and classification [16], so the proposed method can be used to detect an oil dark spot, extract its features and so on.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Based SAR Image Segmentation Method with Weighted Feature

2019

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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.

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Section: Introductionmentioning

confidence: 99%

An Energy-Based SAR Image Segmentation Method with Weighted Feature

2019

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“…There are two ways in which to approach the oil-spill detection problem: studying the characterization of the slick by means of the multi-polarization features of SAR techniques, as occurs in [ 5 , 6 , 7 ], or using the brightness image obtained from the backscatter signal without considering the parameters of the processes of image acquisition and formation, as in [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Our work falls within the second category.…”

Section: Introductionmentioning

confidence: 99%

“…Our work falls within the second category. Most of the approaches based on image processing techniques focus on segmenting SAR images in order to identify the dark-spot region that represents the oil slick [ 8 , 9 , 10 , 11 , 12 ], on extracting features so as to recognize the oil slick from previously segmented images [ 13 , 14 , 15 ], or on classifying dark spots using machine learning techniques [ 16 ], statistical classifiers [ 17 , 18 , 19 ] or/and artificial neural networks [ 20 , 21 , 22 , 23 ]. State-of-the-art methods sometimes use a combination of several approaches, as in [ 24 ], and on other occasions, the image processing methods are combined with Geographic Information Systems (GIS), as in [ 25 ], to provide both the detection and precise location of marine spills.…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

Gil

Alacid

2018

Sensors

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This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images.

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“…To this end, geometry tessellation based segmentation approaches have been proposed, in which image domain is partitioned into a set of sub-regions and image model is built based on it. The commonly used geometric tessellations include regular tessellation (Askari et al, 2013), Voronoi tessellation (Lucarini, 2009;Zhao et al, 2013), Poisson tessellation (Schneider, 2009;Schneider, 2010), and so on. Li et al (2010) proposed a region-based approach for SAR image segmentation based on Voronoi tessellation, Bayesian inference and Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Coupling Regular Tessellation With RJMCMC Algorithm to Segment Sar Image With Unknown Number of Classes

Wang¹,

Li²,

Zhao³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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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SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction

Cited by 19 publications

References 24 publications

An Energy-Based SAR Image Segmentation Method with Weighted Feature

An Energy-Based SAR Image Segmentation Method with Weighted Feature

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

Coupling Regular Tessellation With RJMCMC Algorithm to Segment Sar Image With Unknown Number of Classes

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