A real time operational scheme for ship traffic monitoring using quick look ERS SAR images

Cusano, M.; Lichtenegger, J.; Lombardo, P.; Petrocchi, Andrea; Zanovello, D.

doi:10.1109/igarss.2000.860289

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…Eldhuset [5] using the method to do wake detection in ERS images. Cusano [6] also use this method to do fast wake detection based on the ERS images .…”

Section: Research On Ship Wake In Detection and Recogntionmentioning

confidence: 99%

“…There are a lot of methods to get such kind of features. For example the Fourier transformation [5] or wavelet decomposition [6][7][8] or KL transform [9] and so on. The third kind of features is moving feature, that most of the emerging features are wakes .When moving, there are surface waves , turbulence and internal waves around the ships.…”

Section: The Features Of Ship Targetsmentioning

confidence: 99%

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Study on Ship Target Detection and Recognition in SAR imagery

Wang

Sun

2009

2009 First International Conference on Information Science and Engineering

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Analysis the characteristics of SAR ship target detection and identification. Summarize and divide the commonly used features into three categories : spatial features, transform features and the moving feature. The general ship detection and identification process is discussed in details. The development of SAR ship target detection and identification are surveyed and the mainstream approaches are listed. The main problems on ship detection and recognition process have been described as well. Finally a possible development direction of SAR ship target detection and recognition is given. It is expected that the researchers will obtain useful information from this paper, which will help for their future works and promote the SAR image target detection and recognition of ship development.

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“…Eldhuset [5] using the method to do wake detection in ERS images. Cusano [6] also use this method to do fast wake detection based on the ERS images .…”

Section: Research On Ship Wake In Detection and Recogntionmentioning

confidence: 99%

Section: The Features Of Ship Targetsmentioning

confidence: 99%

Study on Ship Target Detection and Recognition in SAR imagery

Wang

Sun

2009

2009 First International Conference on Information Science and Engineering

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“…Detection of ships in radar clutter by calculating thresholds on clutter probability distribution functions has been extensively studied. Most of the work focuses on CFAR [ 8 ], which includes bi-parameters [ 10 ] and cell-average (CA) algorithms [ 33 ]. To compare the results of 2D-CF with CFAR, the results of bi-parameters CFAR [ 10 ] is presented.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

Improving Ship Detection with Polarimetric SAR based on Convolution between Co-polarization Channels

Wang

2009

Sensors

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The convolution between co-polarization amplitude only data is studied to improve ship detection performance. The different statistical behaviors of ships and surrounding ocean are characterized a by two-dimensional convolution function (2D-CF) between different polarization channels. The convolution value of the ocean decreases relative to initial data, while that of ships increases. Therefore the contrast of ships to ocean is increased. The opposite variation trend of ocean and ships can distinguish the high intensity ocean clutter from ships' signatures. The new criterion can generally avoid mistaken detection by a constant false alarm rate detector. Our new ship detector is compared with other polarimetric approaches, and the results confirm the robustness of the proposed method.

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“…The constant false alarm rate (CFAR) algorithm is a basic method in target detection, which uses CFAR detectors, sliding windows and thresholds to process the amplitude image. Many adaptive threshold algorithms [1, 2] are applied in ship traffic monitoring on SAR images. Although the CFAR algorithms work well in the targets detection, it is not capable to classify different targets because of the lack of classification layer to perform object‐label association [3].…”

Section: Introductionmentioning

confidence: 99%