A complete processing chain for ship detection using optical satellite imagery

Corbane, Christina; Najman, Laurent; Pecoul, Emilien; Demagistri, Laurent; Petit, M.

doi:10.1080/01431161.2010.512310

Cited by 167 publications

(94 citation statements)

References 22 publications

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“…Despite this, often the cloud cover is only partial and is then useful to classify clouds. They come in all shapes and are generally white with redness within 0.45 < NR < 0.6 and therefore difficult to discriminate from boats [10,11] and ice floes. The S2 image in Figure 10 was selected because it contains a large number of smaller clouds, which challenge the classification algorithm.…”

Section: Cloudsmentioning

confidence: 99%

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Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Heiselberg¹,

Heiselberg

2017

Remote Sensing

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Abstract:The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats.

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Section: Cloudsmentioning

confidence: 99%

“…Their frequent transits over the polar regions make these satellites particularly useful for Artic surveillance and for monitoring icebergs, sea-ice coverage [2,3], ships [4][5][6][7][8][9][10][11][12][13][14][15][16][17], oil spills [18][19][20], crop and forestation [21,22].…”

Section: Introductionmentioning

confidence: 99%

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Heiselberg¹,

Heiselberg

2017

Remote Sensing

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“…An operational ship detection algorithm that provides good results also for small targets is detailed in (Corbane et al, 2010). The combined use of optical EO imagery and electronic intelligence satellite data is presented in (Sun et al, 2015).…”

Section: Optical Satellite Data For Ship Detectionmentioning

confidence: 99%

Sentinel-1/2 Data for Ship Traffic Monitoring on the Danube River

Negula¹,

Poenaru²,

Olteanu³

et al. 2016

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

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ABSTRACT:After a long period of drought, the water level of the Danube River has significantly dropped especially on the Romanian sector, in July-August 2015. Danube reached the lowest water level recorded in the last 12 years, causing the blockage of the ships in the sector located close to Zimnicea Harbour. The rising sand banks in the navigable channel congested the commercial traffic for a few days with more than 100 ships involved. The monitoring of the decreasing water level and the traffic jam was performed based on Sentinel-1 and Sentinel-2 free data provided by the European Space Agency and the European Commission within the Copernicus Programme. Specific processing methods (calibration, speckle filtering, geocoding, change detection, image classification, principal component analysis, etc.) were applied in order to generate useful products that the responsible authorities could benefit from. The Sentinel data yielded good results for water mask extraction and ships detection. The analysis continued after the closure of the crisis situation when the water reached the nominal level again. The results indicate that Sentinel data can be successfully used for ship traffic monitoring, building the foundation of future endeavours for a durable monitoring of the Danube River.

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“…New methods are still investigated to deal with these challenges. Corbane et al in (Corbane et al, 2010) describe an automatic detection model based on statistical methods, mathematical morphology and other signal-processing techniques such as the wavelet analysis and Radon transform. They use panchromatic SPOT images with 5 meter resolution.…”

Section: Introductionmentioning

confidence: 99%

Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

Máttyus¹

2013

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

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ABSTRACT:Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000×16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

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A complete processing chain for ship detection using optical satellite imagery

Cited by 167 publications

References 22 publications

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Sentinel-1/2 Data for Ship Traffic Monitoring on the Danube River

Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

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