In today's naval missions, such as anti-piracy or counter-drugs operations, Electro-Optical (EO) sensors play an
increasingly important role. In particular, these sensors are essential for classification and identification of targets.
These tasks are traditionally performed by human operators, but because the complexity of today's missions,
in combination with reduced manning, automating the information processing of EO sensors is increasingly
necessary. This paper discusses the contribution of EO sensor systems to the picture compilation process,
and how the deployment of EO sensors can be optimized for current naval missions. In particular, we discuss
automation techniques for detection, classification and identification using EO sensors. Based on our findings,
we give recommendations for future research.
In the context of naval surveillance, shipboard Electro-Optical (EO) sensor systems can contribute to the detection,
classification and identification of surface objects. Focusing on the detection process, our previous research
offers a method using low-order polynomials for background estimation, which can be used for the automatic
detection of objects in a naval environment. The polynomials are fitted to the intensity values in the image after
which the deviation between the fitted intensity values and the measured intensity values are used for detection.
The research presented in this paper, focuses on the impact of super-resolution algorithms on this detection
process. Images enhanced by SR algorithms are expected to be mainly beneficial for classification purposes,
regardless whether the classification is automatic or operator driven. This paper analyses the influence of SR
algorithms on the detection performance in relation to the increase of computational complexity. The performance
of the detection approach is tested on extensive dataset of maritime pictures in the Mediterranean Sea
and in the North Sea collected on board of a frigate. We have found that for a good super-resolution image in
this environment the sensor should be stabilised while recording and, for fast or near objects or when recording
in heavier weather, should have a high frame rate and/or low exposure times.
For naval surveillance, automatic detection of surface objects, like vessels, in a maritime environment is an
important contribution of the Electro-Optical (EO) sensor systems on board. Based on previous research using
single images, a background estimation approach using low-order polynomials is proposed for the automatic
detection of objects in a maritime environment. The polynomials are fitted to the intensity values in the image
after which the deviation between the fitted intensity values and the measured intensity values are used for detection. The research presented in this paper, includes the time information by using video streams instead of single images. Hereby, the level of fusing time information and the number of frames necessary for stable detection and tracking behaviour are analysed and discussed. The performance of the detection approach is tested on a, during the fall of 2007, collected extensive dataset of maritime pictures in the Mediterranean Sea and in the North Sea on board of an Air Defence Command frigate, HNLMS Tromp.
Due to technical advances and the changing political environment sensor management has become increasingly
knowledge intensive. Aboard navy ships however, we see a decrease of available knowledge, both quantitative and
qualitative. This growing discrepancy drives the need for automation of sensor management. Since the goal of sensor
deployment is to have a complete and accurate operational picture relative to the mission we propose a three-stage sensor
manager, where sensor task requests are generated based on the uncertainty in the (expected) objects' attributes. These
tasks are assigned to available and suited sensors, which in turn are fine-tuned for the task at hand. When trying to
reduce the uncertainty in the classification solution one must first define how the classification process actually works.
We discuss why the classification process needs to be automated as well and show how such classification algorithms
will most likely work in the future.
Automatic detection of surface objects, like vessels, in a maritime environment from images is
an important issue in naval surveillance. Two different approaches - gradient filter and background
estimation - are presented in this paper and the test results on real data, both infrared as well as visible light
images, are discussed. In the gradient approach, a gradient filter scans the sea-part of the image horizontally
and vertically resulting in peaks at locations where the gradient exceeds a predefined local threshold. In the
second approach, the background estimation, a polynomial model of the background is fitted locally to the seapart
of the image. Using these polynomial background-estimators in the actual sea-analysis, objects are
detected. In this paper the advantages and disadvantages of both approaches are discussed.
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