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Experiences from long-range passive and active imaging
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4434-8055
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2015 (English)In: Proceedins of SPIE, 2015, Vol. 9649, 96490J-1-96490J-13 p.Conference paper, Abstract (Refereed)
Abstract [en]

We present algorithm evaluations for ATR of small sea vessels. The targets are at km distance from the sensors, which means that the algorithms have to deal with images affected by turbulence and mirage phenomena. We evaluate previously developed algorithms for registration of 3D-generating laser radar data. The evaluations indicate that some robustness to turbulence and mirage induced uncertainties can be handled by our probabilistic-based registration method.

We also assess methods for target classification and target recognition on these new 3D data. An algorithm for detecting moving vessels in infrared image sequences is presented; it is based on optical flow estimation. Detection of moving target with an unknown spectral signature in a maritime environment is a challenging

problem due to camera motion, background clutter, turbulence and the presence of mirage. First, the optical flow caused by the camera motion is eliminated by estimating the global flow in the image. Second, connected regions containing significant motions that differ from camera motion is extracted. It is assumed that motion caused by a moving vessel is more temporally stable than motion caused by mirage or turbulence. Furthermore, it is assumed that the motion caused by the vessel is more homogenous with respect to both magnitude and orientation, than motion caused by mirage and turbulence. Sufficiently large connected regions with a flow of acceptable magnitude and orientation are considered target regions. The method is evaluated on newly collected sequences of SWIR and MWIR images, with varying targets, target ranges and background clutter.

Finally we discuss a concept for combining passive and active imaging in an ATR process. The main steps are passive imaging for target detection, active imaging for target/background segmentation and a fusion of passive and active imaging for target recognition.

Place, publisher, year, edition, pages
2015. Vol. 9649, 96490J-1-96490J-13 p.
Keyword [en]
Passive imaging, active imaging, data fusion, ATR, MWIR, SWIR, 3D data, target detection, sea vessels.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-128053DOI: 10.1117/12.2196278OAI: oai:DiVA.org:liu-128053DiVA: diva2:928777
Conference
Electro-Optical Remote Sensing, Photonic Technologies, and Applications IX
Available from: 2016-05-16 Created: 2016-05-16 Last updated: 2016-08-31

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Grönwall, Christina
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Automatic ControlFaculty of Science & Engineering
Computer Vision and Robotics (Autonomous Systems)

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