Performance analysis of the TerraSAR-X Traffic monitoring concept

Meyer, Franz J.; Hinz, Stefan; Laika, Andreas; Weihing, Diana; Bamler, Richard

doi:10.1016/j.isprsjprs.2006.08.002

Cited by 69 publications

(52 citation statements)

References 12 publications

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“…Therefore, CFAR classifiers have the advantage of producing statistically consistent results as they do not require a manual and somewhat arbitrary choice of the detection threshold. More information in CFAR classification methods can be found in Gandhi and Kassam (1988);Meyer et al (2006).…”

Section: A Workflow For Automated Landfast Ice Edge Extraction From Imentioning

confidence: 99%

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

Meyer

Mahoney

Eicken

et al. 2011

Remote Sensing of Environment

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In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions.

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Section: A Workflow For Automated Landfast Ice Edge Extraction From Imentioning

confidence: 99%

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

Meyer

Mahoney

Eicken

et al. 2011

Remote Sensing of Environment

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“…The along-track InSAR (AT-InSAR) can measure the velocity of ocean surface currents associated with tides, internal waves, and other oceanic features [63][64][65][66][67][68][69][70][71][72][73], and can also be used as a moving target indicator (MTI) to detect such deterministic (hard) targets as moving vehicles [74][75][76][77][78][79].…”

Section: Sensormentioning

confidence: 99%

“…AT-InSAR can also detect and measure the slant-range velocity of a moving hard targets such as vehicles and ships [74][75][76][77][78][79]. The principle of this technique known as moving target indicator (MTI) by AT-InSAR is essentially the same as that for current measurements.…”

Section: Along-track Insarmentioning

confidence: 99%

Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

2013

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The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin.

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“…During night time or bad weather conditions, active sensors, such as LiDAR [53][54][55] or SAR [56][57][58], may be applicable. Furthermore, infrared sensors are capable of detecting still and recently active vehicles [59].…”

Section: Motivationmentioning

confidence: 99%

An Operational System for Estimating Road Traffic Information from Aerial Images

et al. 2014

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Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic monitoring during disaster and mass events, which is based on an airborne optical sensor system. With this system, optical image sequences are automatically examined on board an aircraft to estimate road traffic information, such as vehicle positions, velocities and driving directions. The traffic information, estimated in real time on board, is immediately downlinked to a ground station. The airborne sensor system consists of a three-head camera system, a real-time-capable GPS/INS unit, five industrial PCs and a downlink unit. The processing chain for automatic extraction of traffic information contains modules for the synchronization of image and navigation data streams, orthorectification and vehicle detection and tracking modules. The vehicle detector is based on a combination of AdaBoost and support vector machine classifiers. Vehicle tracking relies on shape-based matching operators. The processing chain is evaluated on a large number of image sequences recorded during several campaigns, and the data quality is compared to that obtained from induction loops. In summary, we can conclude that the achieved overall quality of the traffic data extracted by the airborne system is in the range of 68% and 81%. Thus, it is comparable to data obtained from stationary ground sensor networks.

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Performance analysis of the TerraSAR-X Traffic monitoring concept

Cited by 69 publications

References 12 publications

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

An Operational System for Estimating Road Traffic Information from Aerial Images

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