Automatic Detection of Ships in RADARSAT-1 SAR Imagery

Wackerman, C.; Friedman, K.S.; Pichel, William G.; Clemente‐Colón, P.; Li, Xiaofeng

doi:10.1080/07038992.2001.10854879

Cited by 76 publications

(47 citation statements)

References 5 publications

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“…In October 1999, National Oceanic and Atmospheric Administration (NOAA), USA, initialized a program called the Alaska SAR Demonstration (AKDEMO) [1]. One of the important AKDEMO goals is to provide ship detection products for users in Alaska in near real time.…”

Section: Resultsmentioning

confidence: 99%

“…The CFAR algorithm is widely used for setting a threshold so that we can find targets that are statistically significant above the background signal while maintaining a constant false alarm rate [1, 12]. A distribution function that fits distribution of region of interest of clutter is first computed for determining the threshold.…”

Section: Methodsmentioning

confidence: 99%

“…SAR images have been widely used for fishing vessel detection, ship traffic monitoring and immigration control [1,2]. Numerous studies have been performed to develop ship detection algorithms in SAR images automatically.…”

Section: Introductionmentioning

confidence: 99%

“…As demonstrated in reference [8], for a multilook SAR intensity image, when the number of looks is over 30, it is suitable to assume the Gaussian PDF of clutter in the multilook image. Therefore, The Gaussian distribution is only valid when a large number of radar looks are averaged [1]. Other distributions such as Gamma or K- distribution have also been suggested [9, 10], but they carry the same limitations as the Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Liao

2008

Sensors

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This paper describes an improved Constant False Alarm Rate (CFAR) ship detection algorithm in spaceborne synthetic aperture radar (SAR) image based on Alpha-stable distribution model. Typically, the CFAR algorithm uses the Gaussian distribution model to describe statistical characteristics of a SAR image background clutter. However, the Gaussian distribution is only valid for multilook SAR images when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian distribution often fails to describe background sea clutter. In this study, we replace the Gaussian distribution with the Alpha-stable distribution, which is widely used in impulsive or spiky signal processing, to describe the background sea clutter in SAR images. In our proposed algorithm, an initial step for detecting possible ship targets is employed. Then, similar to the typical two-parameter CFAR algorithm, a local process is applied to the pixel identified as possible target. A RADARSAT-1 image is used to validate this Alpha-stable distribution based algorithm. Meanwhile, known ship location data during the time of RADARSAT-1 SAR image acquisition is used to validate ship detection results. Validation results show improvements of the new CFAR algorithm based on the Alpha-stable distribution over the CFAR algorithm based on the Gaussian distribution.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

Liao

2008

Sensors

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“…Exploiting the fact that in SAR images ships appear as clusters of bright pixels against the darker background of sea clutter, targets are basically selected by defining a detection threshold based on image statistics. The constant false alarm rate (CFAR) approach (Eldhuset 1996;Wackerman et al 2001) has been and still is the most widely used method, although in the literature a number of alternative techniques can be found, e.g., wavelet-based approaches (Tello, Lopez-Martinez, and Mallorqui 2005), the modified Otsu's algorithm (Messina et al 2012) or the model-based scattering power decomposition (Sugimoto, Ouchi, and Nakamura 2013) for multi-polarization images.…”

Section: Introductionmentioning

confidence: 99%

The exploitation of Sentinel-1 images for vessel size estimation

Stasolla

Greidanus

2016

Remote Sensing Letters

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In this article, a novel technique for fully automatic vessel size estimation using medium-to-high-resolution synthetic aperture radar (SAR) images is presented. Based on mathematical morphology, it aims at better delineating the vessel outline in the cluttered SAR image, thereby enabling the extraction of its actual dimensions. The technique has been tested on a set of 127 ships representing a range in lengths between 24 and 366 m in five Sentinel-1 images at 20 m multilook resolution that have good quality ground truth available. It is found that the proposed algorithm produces very good length estimates (15% relative error/30 m absolute error) and reasonable width estimates (35%/11 m). The estimates are significantly better than those from a simpler automatic method that does not use mathematical morphology, and approach those from manual analysis by an expert. ARTICLE HISTORY

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Satellite Imaging for Maritime Surveillance of the European Seas

Greidanus

2008

Remote Sensing of the European Seas

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Automatic Detection of Ships in RADARSAT-1 SAR Imagery

Cited by 76 publications

References 5 publications

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

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

The exploitation of Sentinel-1 images for vessel size estimation

Satellite Imaging for Maritime Surveillance of the European Seas

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