Enhanced ship detection from overhead imagery

Buck, Heidi; Sharghi, Elan; Guilas, Chessa; Stastny, John; Morgart, William; Schalcosky, Brian; Pifko, Keith

doi:10.1117/12.792058

Cited by 23 publications

(14 citation statements)

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“…Due to the use of the integral images method, the computational effort was rather low. The performance of this method decreased for very dark images so that the threshold was set to s m t ⋅ + = 3 in that case (see also [11]). The binary image that was constructed with the component tree was then processed by the same techniques used for radar images: labelization and clustering [2].…”

Section: Development Of Ship Detection For Rapideyementioning

confidence: 99%

“…Preprocessing may also include filtering in order to smooth background [13] or histogram stretching for contrast improvement [12]. The problem with clouds is approached in literature by filtering in the Fourier domain [11] or by defining a threshold based on the histogram [12]. The techniques described in the literature for the actual detection vary dependent on the available images as well as specific application areas.…”

Section: Development Of Ship Detection For Rapideyementioning

confidence: 99%

“…For example, in [13] a method is described that combines images from different spectral regions which may not always be available. In [9] and [11] images are first characterized into "grey" or "black" images, and the detection is implemented based on image processing methods like thresholding, edge detection and dilation. The application of Genetic Algorithms and Neural Networks for ship characterization showed dissatisfactory results in [14].…”

Section: Development Of Ship Detection For Rapideyementioning

confidence: 99%

“…For preprocessing some preparatory techniques are described in the literature like land masking and dividing images into smaller parts to gain advantages regarding processing time and local application of operators [9,11,12]. Preprocessing may also include filtering in order to smooth background [13] or histogram stretching for contrast improvement [12].…”

Section: Development Of Ship Detection For Rapideyementioning

confidence: 99%

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Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project

Saur

Estable

Zielinski

et al. 2011

OCEANS 2011 IEEE - Spain

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The project DEKO (Detection of artificial objects in sea areas) is integrated in the German DeMarine-Security project and focuses on the detection and classification of ships and offshore artificial objects relying on TerraSAR-X as well as on RapidEye multispectral optical images. The objectives are 1/ the development of reliable detection algorithms and 2/ the definition of effective, customized service concepts. In addition to an earlier publication, we describe in the following paper some selected results of our work. The algorithms for TerraSAR-X have been extended to a processing chain including all needed steps for ship detection and ship signature analysis, with an emphasis on object segmentation. For Rapid Eye imagery, a ship detection algorithm has been developed. Finally, some applications are described: Ship monitoring in the Strait of Dover based on TerraSAR-X StripMap using AIS information for verification, analyzing TerraSAR-X HighResolution scenes of an industrial harbor and finally an example of surveying a wind farm using change detection

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Section: Development Of Ship Detection For Rapideyementioning

confidence: 99%

Section: Development Of Ship Detection For Rapideyementioning

confidence: 99%

Section: Development Of Ship Detection For Rapideyementioning

confidence: 99%

Section: Development Of Ship Detection For Rapideyementioning

confidence: 99%

See 2 more Smart Citations

Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project

Saur

Estable

Zielinski

et al. 2011

OCEANS 2011 IEEE - Spain

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“…[1][2][3] The FMV capability focuses on automatically detecting and tracking maritime targets (against a maritime background) in full-motion video; it is intended to be a video analysis tool for extracting meaningful data from FMV with minimal human supervision.…”

Section: Introductionmentioning

confidence: 99%

Marine object detection in UAV full-motion video

Parameswaran¹,

Lane²,

Bagnall³

et al. 2014

SPIE Proceedings

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Recent years have seen an increased use of Unmanned Aerial Vehicles (UAV) with video-recording capability for Maritime Domain Awareness (MDA) and other surveillance operations. In order for these efforts to be effective, there is a need to develop automated algorithms to process the full-motion videos (FMV) captured by UAVs in an efficient and timely manner to extract meaningful information that can assist human analysts and decision makers. This paper presents a generalizeable marine object detection system that is specifically designed to process raw video footage streaming from UAVs in real-time. Our approach does not make any assumptions about the object and/or background characteristics because, in the MDA domain, we encounter varying background and foreground characteristics such as boats, bouys and ships of varying sizes and shapes, wakes, white caps on water, glint from the sun, to name but a few. Our efforts rely on basic signal processing and machine learning approaches to develop a generic object detection system that maintains a high level of performance without making prior assumptions about foreground-background characteristics and does not experience abrupt performance degradation when subjected to variations in lighting, background characteristics, video quality, abrupt changes in video perspective, size, appearance and number of the targets. In the following report, in addition to our marine object detection system, we present representative object detection results on some real-world UAV full-motion video data.

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Maritime anomaly detection: A review

Riveiro¹,

Pallotta

Vespe

2018

WIREs Data Min & Knowl

113

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The surveillance of large sea areas normally requires the analysis of large volumes of heterogeneous, multidimensional and dynamic sensor data, in order to improve vessel traffic safety, maritime security and to protect the environment. Early detection of conflict situations at sea provides critical time to take appropriate action with, possibly before potential problems occur. In order to provide an overview of the state‐of‐the‐art of research carried out for the analysis of maritime data for situational awareness, this study presents a review of maritime anomaly detection. The found articles are categorized into four groups (a) data, (b) methods, (c) systems, and (d) user aspects. We present a comprehensive summary of the works found in each category, and finally, outline possible paths of investigation and challenges for maritime anomaly detection. This article is categorized under: Application Areas > Government and Public Sector Algorithmic Development > Spatial and Temporal Data Mining

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Enhanced ship detection from overhead imagery

Cited by 23 publications

References 0 publications

Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project

Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project

Marine object detection in UAV full-motion video

Maritime anomaly detection: A review

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