&lt;title&gt;Land mine detection with an ultra-wideband SAR system&lt;/title&gt;

Andrieu, Joël; Gallais, F.; Mallepeyre, V.; Bertrand, Valérie; Beillard, Bruno; Jecko, Bernard; Guillerey, R.; LeGoff, M.

doi:10.1117/12.479094

Cited by 17 publications

(4 citation statements)

References 5 publications

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“…It is the trend to use vehicle-or air-borne Ultra-Wide Band Synthetic Aperture Radar (UWB SAR) to detection landmines over large areas quickly [1,2,3,4,5]. The major challenge for UWB SAR landmine detection is too many false alarms in detection results.…”

Section: Introductionmentioning

confidence: 99%

Aspect-invariant feature extraction and associated landmine detector in UWB SAR

Jin

Zhang

Chang

et al. 2006

2006 CIE International Conference on Radar

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Vehicle-or air-borne Ultra-Wide Band Synthetic Aperture Radar (UWB SAR) can perform landmine detection over large areas, where too many false alarms is the major problem for UWB SAR application in practice. In this paper, landmine aspect-invariant feature extraction using the Space-Wavenumber Distribution (SWD) and its associated Support Vector Machine (SVM) with a hypersphere classification boundary are proposed in landmine detection procedure. The proposed methods have been tested by the real data collected with the Rail-GPSAR system..

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Section: Introductionmentioning

confidence: 99%

Aspect-invariant feature extraction and associated landmine detector in UWB SAR

Jin

Zhang

Chang

et al. 2006

2006 CIE International Conference on Radar

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“…However, limited by the azimuth resolution of subaperture images, N is generally small. As a result, BOR and anisotropy targets may not be well distinguished using (2). Assume that the aperture collected from FDTD simulation is divided into 9 subapertures, the metrics of the near and far peaks of the cylinder and the trihedral using (2) …”

Section: Metric Of Aspect-invariant Characteristic Based On Asementioning

confidence: 99%

“…These characteristics guarantee better soil or foliage penetration, higher resolution of SAR images, and richer aspect-dependent information of targets, making UWB SAR a promising method for detecting targets from a safe standoff distance [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Body-of-Revolution-Enhanced Imaging in Ultra-Wideband Sar

Liang¹,

Song²,

Zhang³

et al. 2013

PIER

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Abstract-For ultra-wideband synthetic aperture radar (UWB SAR), there often exist a lot of clutters in the image, and the weak targets are easy to be masked by them. However, using the prior scattering knowledge of targets, enhanced imaging can be realized and beneficial improvements in image quality and detection performance can be expected. In this paper, an enhanced imaging method for body of revolution (BOR) has been researched. Since the BOR target has the unique feature of aspect-invariant characteristic, the aspect scattering entropy (ASE) is proposed to describe the diversity of aspect scattering and used in the BOR-enhanced imaging method. Then the application of the proposed method in landmine detection is discussed. The experimental results show that the BOR targets are effectively enhanced and the clutters are surpressed and thus the probability of landmine detection increases under the same false alarm rate.

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“…Ultra-Wide Band Synthetic Aperture Radar (UWB SAR) with the ground penetrating capability has become an alternative way to detect landmines over large areas [1][2][3]. The typical detection procedure is first to extract the Regions of Interest (ROIs) from the entire SAR image, which is called prescreening in this paper.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction for Landmine Detection in Uwb Sar via SWD and Isomap

Liu¹,

Tian²,

Zhang³

2013

PIER

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Abstract-Ultra-wideband synthetic aperture radar (UWB SAR) is a sufficient approach to detect landmines over large areas from a safe standoff distance. Feature extraction is the key step of landmine detection processing. On the one hand, the feature vector should contain more scattering characteristics to discriminate landmines from clutters; on the other hand, the dimensionality of feature vector should be lower to avoid the "curse of dimensionality". In this paper, a novel feature vector extraction method is proposed. We first obtain the scattering characteristics in four domains, i.e., range, azimuth, frequency and aspect-angle, via the space-wavenumber distribution (SWD). Since the data after SWD are with higher dimension and local nonlinear structures, a typical manifold learning method, Isomap, is used to reduce the dimension. The validity of the proposed method is proved by using the real data collected by an airship-borne UWB SAR system.

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<title>Land mine detection with an ultra-wideband SAR system</title>

Cited by 17 publications

References 5 publications

Aspect-invariant feature extraction and associated landmine detector in UWB SAR

Aspect-invariant feature extraction and associated landmine detector in UWB SAR

Body-of-Revolution-Enhanced Imaging in Ultra-Wideband Sar

Feature Extraction for Landmine Detection in Uwb Sar via SWD and Isomap

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