Results of optical detection trials in harbour environment

Kemp, Rob A. W.; Groot, Johannes F. de; Broek, Sebastiaan P. van den; Lange, Dirk-Jan J. de; Dijk, J. C. van; Schwering, Piet B. W.

doi:10.1117/12.778185

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…For the experiments, data was used recorded in Rotterdam [10] using a long-wave infrared camera. Three types of ships are used in a harbor scenario: a water taxi (similar to a cabin boat), a rhib and a jet-ski.…”

Section: Resultsmentioning

confidence: 99%

Discrimination of classes of ships for aided recognition in a coastal environment

Broek

Bouma

Degache

et al. 2009

Automatic Target Recognition XIX

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For naval operations in a coastal environment, detection of boats is not sufficient. When doing surveillance near a supposedly friendly coast, or self protection in a harbor, it is important to find the one object that means harm, among many others that do not. For this, it is necessary to obtain information on the many observed targets, which in this scenario are typically small vessels. Determining the exact type of ship is not enough to declare it a threat. However, in the whole process from (multi-sensor) detection to the decision to act, classification of a ship into a more general class is already of great help, when this information is combined with other data to assist an operator.We investigated several aspects of the use of electro-optical systems. As for classification, this paper concentrates on discriminating classes of small vessels with different electro-optical systems (visual and infrared) as part of the larger process involving an operator. It addresses both selection of features (based on shape and texture) and ways of using these in a system to assess threats. Results are presented on data recorded in coastal and harbor environments for several small targets.

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Section: Resultsmentioning

confidence: 99%

Discrimination of classes of ships for aided recognition in a coastal environment

Broek

Bouma

Degache

et al. 2009

Automatic Target Recognition XIX

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“…The above mentioned turbulence is a typical example of this, but it can also imply for instance the automatic enabling of Dynamic Super Resolution in order to generate more resolution in the images for more distant targets. A full description of potential algorithms of use in the pre-processing stage is given in other papers 6,7 .…”

Section: Sensor Managementmentioning

confidence: 99%

Task-specific sensor settings for electro-optical systems in a marine environment

et al. 2010

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Present-day naval operations take place in coastal environments as well as narrow straits all over the world. Coastal environments around the world are exhibiting a number of threats to naval forces. In particular, a large number of asymmetric threats can be present in environments with cluttered backgrounds as well as rapidly varying atmospheric conditions. The automatic detection of small targets by electro-optical systems may be hampered by small surface structure variations at the surface and near the horizon.In current electro-optical sensor systems processing of imagery is seldom task-specific. Using task-specific settings of sensors, processing and fusion, can improve the performance of electro-optical systems dramatically. This paper discusses the effect of dynamic sensor settings as function of specific tasks and environmental parameters and how these can play a role in the management of sensors in a naval application. In addition, a series of experiments with different targets are presented to demonstrate the benefit of sensor management. Some sensor management approaches for application in infrared systems are discussed.

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“…Therefore, we used data from several trials, including data from SURFER [2], SPITS [19], and the Rotterdam harbor [14]. This data contains information from different weather conditions, open sea and harbor environment, and several imaging artifacts.…”

Section: Background Estimationmentioning

confidence: 99%

“…The system should be able to detect and classify surface targets in maritime environments. Our system concept consists of an image processing chain [17], which includes components that perform image enhancement [14], automatic detection, clutter reduction and classification [3].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of small surface targets with electro-optical sensors in a harbor environment

et al. 2008

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In modern warfare scenarios naval ships must operate in coastal environments. These complex environments, in bays and narrow straits, with cluttered littoral backgrounds and many civilian ships may contain asymmetric threats of fast targets, such as rhibs, cabin boats and jet-skis. Optical sensors, in combination with image enhancement and automatic detection, assist an operator to reduce the response time, which is crucial for the protection of the naval and land-based supporting forces. In this paper, we present our work on automatic detection of small surface targets which includes multi-scale horizon detection and robust estimation of the background intensity. To evaluate the performance of our detection technology, data was recorded with both infrared and visual-light cameras in a coastal zone and in a harbor environment. During these trials multiple small targets were used. Results of this evaluation are shown in this paper.

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Results of optical detection trials in harbour environment

Cited by 5 publications

References 8 publications

Discrimination of classes of ships for aided recognition in a coastal environment

Discrimination of classes of ships for aided recognition in a coastal environment

Task-specific sensor settings for electro-optical systems in a marine environment

Automatic detection of small surface targets with electro-optical sensors in a harbor environment

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