Moving Target Tracking in Single- and Multichannel SAR

Henke, Daniel; Domínguez, Elías Méndez; Small, David; Schaepman, Michael E.; Meier, Erich

doi:10.1109/tgrs.2014.2369060

Cited by 44 publications

(14 citation statements)

References 23 publications

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“…Absolute geometric calibration was achieved by using the geolocation of the corner reflectors deployed along the area of interest, giving residual range and azimuth geolocation errors below 2 centimetres in each dimension. The RAW data was processed with a matched filter without taper in range, and an interpolated matched filter (Graphics Processor Unit-based back-projection) with a Hanning tapper in azimuth (Henke, 2015). Table 1.…”

Section: Data and Test Sitesmentioning

confidence: 99%

High Resolution Airborne Sar Image Change Detection in Urban Areas With Slightly Different Acquisition Geometries

Domínguez

Henke

Small

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Before applying change detection, high resolution SAR (Synthetic Aperture Radar) imagery benefits from advanced denoising mechanisms to preserve details and minimize speckle. We propose a change detector based on a MCA (Morphological Components Analysis) of a difference image (DI). With MCA, the data is decomposed into image features utilizing sparse representations of the image content. By introducing a priori knowledge of the content of the scenes, and exploiting shape information corresponding to the changes provided by MCA, we can significantly improve performance under adverse conditions, such as inconsistent acquisition geometries.

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Section: Data and Test Sitesmentioning

confidence: 99%

High Resolution Airborne Sar Image Change Detection in Urban Areas With Slightly Different Acquisition Geometries

Domínguez

Henke

Small

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In [4], integration methods for a combined single-channel and multi-channel target tracking were based solely on the center of mass. This can lead to gaps in the trajectory of an object, especially in the transitions between ATI-supported and single-channel-supported observations.…”

Section: Extended Target Tracking Algorithmmentioning

confidence: 99%

“…To overcome that, we use additional features -namely phase ɸ, intensity I and size A of an object -to merge the two observation sets in a more robust way. Starting from the model with only the center of mass ( [4]), different possibilities of embedding the new combined observation (≙ feature) set into the tracking model are conceivable. We chose a solution where the size, intensity and phase are included in the state space:…”

Section: Extended Target Tracking Algorithmmentioning

confidence: 99%

“…The state transition function f(x) was assumed to show a constant temporal behavior of the newly integrated observation set. Given this definition of the state and state transition function, the transformation function h(x) mapping the state into the observation state can be derived, and the state is updated according to the Kalman gain (for equations, see [4]). Including combined features from both the along-track interferometry (ATI) and single-channel data not only in the detection step but also in the temporal tracking model, allows a more robust estimation of the trajectories.…”

Section: Extended Target Tracking Algorithmmentioning

confidence: 99%

“…Furthermore, SAR data can be acquired almost independently of weather conditions and daylight. In [3] and [4], we developed a flexible GMTI approach which -compared to traditional SAR/GMTI -is capable of not only indicating the presence of moving targets, but also the extraction of an object's motion while in the beam of the SAR sensor by making use of temporal processing methods. Based on these findings, in this paper, we enhance the method of [4] using improved methods of combining single-channel and alongtrack interferometric data for more robust trajectory estimations.…”

Section: Introductionmentioning

confidence: 99%

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Tracking and refocussing of moving targets in multichannel SAR data

Henke

Meier

2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Moving targets in Synthetic Aperture Radar (SAR) data impose several challenges for SAR processing. In full-aperture SAR images, moving objects appear smeared and/or displaced. In previous studies, we showed that a combination of sub-aperture processing, feature extraction, unscented Kalman filtering and multi-target hypotheses can reliably detect and track moving objects. In this paper, we present some extensions to the tracking algorithm itself, show results on a newly acquired data set and demonstrate how the estimated trajectories of the moving objects can be utilized to refocus the originally smeared objects. TRACKING AND REFOCUSSING OF MOVING TARGETS IN MULTICHANNEL SAR DATA Daniel Henke, Erich MeierRemote Sensing Laboratories (RSL), University of Zurich ABSTRACTMoving targets in Synthetic Aperture Radar (SAR) data impose several challenges for SAR processing. In fullaperture SAR images, moving objects appear smeared and/or displaced. In previous studies, we showed that a combination of sub-aperture processing, feature extraction, unscented Kalman filtering and multi-target hypotheses can reliably detect and track moving objects. In this paper, we present some extensions to the tracking algorithm itself, show results on a newly acquired data set and demonstrate how the estimated trajectories of the moving objects can be utilized to refocus the originally smeared objects.

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