Sparse representations for automatic target classification in SAR images

Thiagarajan, Jayaraman J.; Ramamurthy, Karthikeyan Natesan; Knee, Peter; Spanias, Andreas; Berisha, Visar

doi:10.1109/isccsp.2010.5463416

Cited by 143 publications

(107 citation statements)

References 10 publications

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“…The non-convex 0 -norm objective in Equation (1) is an NP-hard problem. Typical approaches for solving the problem are either approximating the original problem with 1 -norm based convex relaxation [9] or resorting to greedy schemes, such as orthogonal matching pursuit (OMP) [8]. After solving the optimal representationx, SRC decides the identity of the test sample by evaluating which class of samples could result in the minimum reconstruction error.…”

Section: Sparse Representation-based Classification (Src)mentioning

confidence: 99%

“…Other features, such as geometrical descriptors [3], attributed scattering centers [4,5], and monogenic spectrums [6], are also applied to SAR target recognition. As for the decision engines, various classifiers, including support vector machines (SVM) [7], sparse representation-based classification (SRC) [8,9], and convolutional neural networks (CNN) [10] are employed for target recognition and have achieved delectable results. Despite great effort, SAR target recognition under extended operating conditions (EOCs) [1] is still a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Multi-View SAR Images for Robust Target Recognition

Ding

Wen

2017

Remote Sensing

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Abstract:The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method.

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Section: Sparse Representation-based Classification (Src)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploiting Multi-View SAR Images for Robust Target Recognition

Ding

Wen

2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Since the manifold of given classes that is closest to the query sample could be found by the sparse representation [29], SRC has been applied to separate features of different targets. In contrast to SRC, in this paper, a local sparse representation model, LSR, is proposed to limit the feasible set of representations.…”

Section: The Comparison Of Classification Modelmentioning

confidence: 99%

“…In recent years, the sparse representation-based classification (SRC) [28] technique has achieved some impressive performances on face recognition. Jayaraman et al [29] reported that the classification of SAR images based on SRC is equivalent to finding the manifold that is closest to the query image. The sparse signal representation can be applied in SAR target classification, as verified in [8,30].…”

Section: Introductionmentioning

confidence: 99%

SAR Target Recognition via Local Sparse Representation of Multi-Manifold Regularized Low-Rank Approximation

Dong

Fan

et al. 2018

Remote Sensing

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Abstract:The extraction of a valuable set of features and the design of a discriminative classifier are crucial for target recognition in SAR image. Although various features and classifiers have been proposed over the years, target recognition under extended operating conditions (EOCs) is still a challenging problem, e.g., target with configuration variation, different capture orientations, and articulation. To address these problems, this paper presents a new strategy for target recognition. We first propose a low-dimensional representation model via incorporating multi-manifold regularization term into the low-rank matrix factorization framework. Two rules, pairwise similarity and local linearity, are employed for constructing multiple manifold regularization. By alternately optimizing the matrix factorization and manifold selection, the feature representation model can not only acquire the optimal low-rank approximation of original samples, but also capture the intrinsic manifold structure information. Then, to take full advantage of the local structure property of features and further improve the discriminative ability, local sparse representation is proposed for classification. Finally, extensive experiments on moving and stationary target acquisition and recognition (MSTAR) database demonstrate the effectiveness of the proposed strategy, including target recognition under EOCs, as well as the capability of small training size.

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“…There are many algorithms for SAR target feature extraction, such as principal component analysis (PCA) [1][2][3], independent component analysis (ICA) [4][5][6], Hu invariant moments [7], non-negative matrix factorization (NMF) [8][9][10] and sparse representation (SR) [11][12][13], etc. As for the target classification, theoretically any classification algorithms applied to pattern recognition can also be extended to the application of SAR image.…”

Section: Introductionmentioning

confidence: 99%

SAR Automatic Target Recognition Based on Deep Convolutional Neural Networks

Zhan¹,

Tian²,

Hu³

et al. 2017

dtcse

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Abstract. Deep convolutional neural networks (CNN) have recently proven extremely competitive in challenging visible light image and speech recognition tasks. The goal of the present study is to explore the application of automatically learned convolutional network features to radar target recognition. Specifically, a two-stage convolutional-pooling network architecture is designed and error back-propagation algorithm with momentum acceleration strategy is used to learn the network weights in a supervised fashion. The effectiveness of the proposed method is assessed by SAR image classification tasks on the standard benchmark of MSTAR (Moving and Stationary Target Acquisition and recognition) database. Our experiments show the presented method has achieved encouraging results with a correct recognition rate of 95.64% for three classes of targets and 92.86% for ten classes of targets.

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Sparse representations for automatic target classification in SAR images

Cited by 143 publications

References 10 publications

Exploiting Multi-View SAR Images for Robust Target Recognition

Exploiting Multi-View SAR Images for Robust Target Recognition

SAR Target Recognition via Local Sparse Representation of Multi-Manifold Regularized Low-Rank Approximation

SAR Automatic Target Recognition Based on Deep Convolutional Neural Networks

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