&lt;title&gt;Template-based SAR ATR performance using different image enhancement techniques&lt;/title&gt;

Owirka, G.J.; Verbout, S.M.; Novak, Leslie M.

doi:10.1117/12.357648

Cited by 49 publications

(24 citation statements)

References 8 publications

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“…A data adaptive approach has been developed to enhance SAR image resolution, based on a superresolution technique called high definition imaging (HDI). In [2] it is demonstrated that superresolution algorithms can improve ATR performance. Prior to performing ATR, HDI is applied to data to enhance SAR image resolution.…”

Section: Literature Survey Of Target Classification Recognition Andmentioning

confidence: 98%

Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance

Suvorova¹,

Schroeder²

2002

Digital Signal Processing

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Section: Literature Survey Of Target Classification Recognition Andmentioning

confidence: 98%

Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance

Suvorova¹,

Schroeder²

2002

Digital Signal Processing

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“…Feature extraction has confirmed its usage in synthetic aperture radar (SAR) target recognition and classification, where a given target is classified as a specific target type by feature matching over the known database [1][2][3][4][5]. In fact, the high-frequency scattering response of a target is well approximated as a sum of response from individual scattering centers [6].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction of SAR scattering centers using M-RANSAC and STFRFT-based algorithm

Song

Gao

Zhu

et al. 2016

EURASIP J. Adv. Signal Process.

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This paper introduces a modified random sample consensus (M-RANSAC) and short-time fractional Fourier transform (STFRFT)-based algorithm for feature extraction of synthetic aperture radar (SAR) scattering centers. In this algorithm, the range migration curve (RMC) of a scattering center is formulated as a parametric model. By estimating these parameters, the backscattering envelope of scattering center, corresponding to the backscattering variation in synthetic aperture time, is extracted directly from a time-domain range-compressed signal. The estimated parameters can also reconstruct the geographical location and along-track velocity of scattering centers. Thus, even without knowing explicit knowledge of platform velocity and forming a SAR image, this algorithm is capable of realizing feature extraction. To estimate parameters scatter by scatter, M-RANSAC approach is proposed as an implementary method with iterative procedure. In the iterations, fitting precision indicator (FPI) works cooperatively with construction fitness coefficient (CFC) to determine the optimal parameters of different scattering centers. Adapting this method to more general cases, STFRFT is introduced to separate the overlapped trajectories of RMCs of scattering centers. The root mean squared errors (RMSEs) of parameter estimation are close to their Cramér-Rao lower bounds (CRLB). The effectiveness of feature extraction based on the devised algorithm is validated by both simulated and real SAR data.

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“…At present, common methods of Automatic Target Recognition technology of SAR images (SAR-ATR) include template matching [1], Support Vector Machine (SVM) [2], Linear interpolation [3], Principal Component Analysis [4], etc. These methods have been successful in some way, but they rely heavily on experience of experts [5].…”

Section: I! Introductionmentioning

confidence: 99%

Combining Deep Convolutional Neural Network and SVM to SAR Image Target Recognition

Gao

Huang

Wang

et al. 2017

2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and I

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<title>Template-based SAR ATR performance using different image enhancement techniques</title>

Cited by 49 publications

References 8 publications

Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance

Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance

Feature extraction of SAR scattering centers using M-RANSAC and STFRFT-based algorithm

Combining Deep Convolutional Neural Network and SVM to SAR Image Target Recognition

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