Target feature extraction using time-frequency representation in synthetic aperture radar

Jin, Tian; Zhang, Zhimin; Chang, Wenge; Huang, Xiaotao

doi:10.1117/12.655045

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Based on the Space-Wavenumber Transform (SWT), the slant-range profiles with respect to some aspect angles and frequencies of the i-th suspected target are extracted [10] . The profiles are connected from end to end to form the feature vector,…”

Section: Optimal Hypersphere In the Input Spacementioning

confidence: 99%

A novel SVM for Ground Penetrating Synthetic Aperture Radar landmine detection

Jin

Zhang

Song

et al. 2008

J. Electron.(China)

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The use of vehicle-or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.

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Section: Optimal Hypersphere In the Input Spacementioning

confidence: 99%

A novel SVM for Ground Penetrating Synthetic Aperture Radar landmine detection

Jin

Zhang

Song

et al. 2008

J. Electron.(China)

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“…Based on the BP imaging model [11], 0 and f have relationship with kr and ky as 0 = arctan(ky / kr ) (2) with c being the speed of electromagnetic wave in free space.…”

Section: Aspect-invariant Feature Extractionmentioning

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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