Rethinking vehicle classification with wide-angle polarimetric SAR

Saville, Michael A.; Jackson, Julie Ann; Fuller, Dane F.

doi:10.1109/maes.2014.130057

Cited by 49 publications

(30 citation statements)

References 14 publications

(23 reference statements)

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“…Civilian vehicles contain a doublebounce scatterer between the vehicle's side and the ground as well as a single-bounce cylinder scattering mechanism from the vehicle roofline [19]. Because we can neither report the posterior or credible set efficiently for the M = 8…”

Section: B Qbcse Compared To Random Samplingmentioning

confidence: 99%

Quadrature-based credible set estimation for radar feature extraction

Rademacher

Jackson

Rexford

et al. 2015

2015 IEEE Radar Conference (RadarCon)

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Abstract-Efficient and accurate extraction of physicallyrelevant features from measured radar data is desirable for automatic target recognition (ATR). In this paper, we present an estimation technique to find credible sets of parameters for any given feature model. The proposed approach provides parameter estimates along with confidence values. Maximum a posteriori (MAP) estimates provide a single (vector) parameter value, typically found via sampling methods. However, computational inefficiency and inaccuracy issues commonly arise when sampling multi-modal or multi-dimensional posteriors. As an alternative, we use Gaussian quadrature to compute probability mass functions, covering the entire probability space. An efficient zoom-in approach is used to iteratively locate regions of high probability. The (possibly disjoint) regions of high probability correspond to sets of feasible parameter values, call credible sets. Thus, our quadrature-based credible set estimator (QBCSE) includes values very near the true parameter and confuser values that may lie far from the true parameter but map with high probability to the same observed data. The credible set and associated probabilities are computed and should both be passed to an ATR algorithm for informed decision-making. Applicable to any feature model, we demonstrate the proposed QBCSE scheme using canonical shape feature models in synthetic aperture radar phase history.

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Section: B Qbcse Compared To Random Samplingmentioning

confidence: 99%

Quadrature-based credible set estimation for radar feature extraction

Rademacher

Jackson

Rexford

et al. 2015

2015 IEEE Radar Conference (RadarCon)

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“…They published Target Discrimination Research Challenge and the corresponding data subset [30]. Subsequently, different researchers have studied WSAR target recognition [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Peng

2019

Electronics

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Target recognition is an important area in Synthetic Aperture Radar (SAR) research. Wide-angle Synthetic Aperture Radar (WSAR) has obvious advantages in target imaging resolution. This paper presents a vehicle target recognition algorithm for wide-angle SAR, which is based on joint feature set matching (JFSM). In this algorithm, firstly, the modulus stretch step is added in the imaging process of wide-angle SAR to obtain the thinned image of vehicle contour. Secondly, the gravitational-based speckle reduction algorithm is used to obtain a clearer contour image. Thirdly, the image is rotated to obtain a standard orientation image. Subsequently, the image and projection feature sets are extracted. Finally, the JFSM algorithm, which combines the image and projection sets, is used to identify the vehicle model. Experiments show that the recognition accuracy of the proposed algorithm is up to 85%. The proposed algorithm is demonstrated on the Gotcha WSAR dataset.

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“…For more efficient interpretation of SAR data, one often need to first recognize the semantic category of a scene and then discover the semantically meaningful information contained within scenes [1][2][3][4][5], for target detection, target segmentation, target recognition and so on. Consequently, scene classification has been one of the most fundamental tasks in SAR images understanding and interpretation [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Sparse Robust Filters for scene classification of Synthetic Aperture Radar (SAR) images

et al. 2016

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With the increasing resolution of Synthetic Aperture Radar (SAR) images, extracting their discriminative features for scenes classification has become a challenging task, because SAR images are very sensitive to target aspect brought by shadowing effects, interaction of the signature with the environment, and so on. Moreover, SAR images are remarkably polluted by the multiplicative speckle noise, which makes the conventional feature extractors inefficient. In this paper we advance new Sparse Robust Filters (SRFs) for automatic learning of discriminant features of scenes. A Hierarchical Group Sparse Coding (HGSC) model is proposed to learn a set of sparse and robust filters, to capture the multiscale local descriptors that are robust to noises. Some experiments are taken on a TerraSAR-X images dataset (in the middle of the Swabian Jura, the Nördlinger Ries, HH, observed on July, 2007), and a Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, to evaluate the performance of our proposed method. The experimental results show that our method can achieve higher classification accuracy compared with other related approaches.

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Rethinking vehicle classification with wide-angle polarimetric SAR

Cited by 49 publications

References 14 publications

Quadrature-based credible set estimation for radar feature extraction

Quadrature-based credible set estimation for radar feature extraction

A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Sparse Robust Filters for scene classification of Synthetic Aperture Radar (SAR) images

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