Ship Detection in SAR Image Based on the Alpha-stable Distribution

Wang, Changcheng; Liao, Mingsheng; Li, Xiaofeng

doi:10.3390/s8084948

Cited by 89 publications

(43 citation statements)

References 14 publications

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“…It has been used for background clutter modeling in a ship detection scheme proposed by Liao and Wang [55,56], where the images dealt with issues of inhomogeneity and spiky properties. The ability this distribution has to fit heavy-tailed models, due in part to the spiky appearance of inhomogeneous sea-clutter, results in a better modeling performance than the ones obtained by the classical models to which it was compared.…”

Section: Statistical Distributionsmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

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Section: Statistical Distributionsmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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“…The background clutters are usually modeled by a Gaussian distribution. However, for complicated scenes, other models, such as Gamma, Weibull, K and Alpha-stable distribution, are recommended in [7][8][9][10][11][12], respectively. For these types of methods, it is computationally expensive to estimate the distribution parameters of complex distribution models, and the accuracy of parameter estimation also has significant influences on the final detection results.…”

Section: Introductionmentioning

confidence: 99%

Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision

et al. 2015

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The robust detection of ships is one of the key techniques in coastal and marine applications of synthetic aperture radar (SAR). Conventional SAR ship detectors involved multiple parameters, which need to be estimated or determined very carefully. In this paper, we propose a new ship detection approach based on multi-scale heterogeneities under the a contrario decision framework, with a few parameters that can be easily determined. First, multi-scale heterogeneity features are extracted and fused to build a heterogeneity map, in which ships are well highlighted from backgrounds. Second, a set of reference objects are automatically selected by analyzing the saliency of local regions in the heterogeneity map and then are used to construct a null hypothesis model for the final decision. Finally, the detection results are obtained by using an a contrario decision. Experimental results on real SAR images demonstrate that the proposed method not only works more stably for ships with different sizes, but also has better performance than conventional ship detectors.

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“…At present, there have been many literatures on ship detection in SAR imagery. The early researches are generally based on intensity images [1][2][3][4]. As early as 1996, an automatic ship and ship wake detection system for spaceborne SAR image is proposed by Eldhuset [1].…”

Section: Introductionmentioning

confidence: 99%

An Improved Shape Contexts Based Ship Classification in SAR Images

et al. 2017

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Abstract:In synthetic aperture radar (SAR) imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to fully exploit the distinguishing characters of the ship targets, both topology and intensity of the scattering points of the ship are considered. The results of testing the proposed method on a data set of three types of ships, collected via a space-borne SAR sensor designed by the Institute of Electronics, Chinese Academy of Sciences (IECAS), establish that the proposed method is superior to several existing methods, including the original shape contexts method, traditional invariant moments and the recent approach.

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Ship Detection in SAR Image Based on the Alpha-stable Distribution

Cited by 89 publications

References 14 publications

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision

An Improved Shape Contexts Based Ship Classification in SAR Images

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