Automatic target recognition with joint sparse representation of heterogeneous multi-view SAR images over a locally adaptive dictionary

Cao, Zongjie; Xu, Liyuan; Feng, Jilan

doi:10.1016/j.sigpro.2015.12.018

Cited by 42 publications

(33 citation statements)

References 22 publications

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“…The images were captured at two different depression angles (15 • and 17 • ) with 190 ∼ 300 different aspect versions, which provide full aspect coverage over 360 • [26]. Similar to the earlier experiments, the images with depression angle of 17 • are used as training set, and the images with depression angle of 15 • are used for the test, as shown in Table 2 [27]. We read the image from the RAW format slice data.…”

Section: Methodsmentioning

confidence: 99%

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

et al. 2018

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In synthetic aperture radar (SAR) target recognition, the amount of target data increases continuously, and thus SAR automatic target recognition (ATR) systems are required to provide updated feature models in real time. Most recent SAR feature extraction methods have to use both existing and new samples to retrain a new model every time new data is acquired. However, this repeated calculation of existing samples leads to an increased computing cost. In this paper, a dynamic feature learning method called incremental nonnegative matrix factorization with L p sparse constraints (L p-INMF) is proposed as a solution to that problem. In contrast to conventional nonnegative matrix factorization (NMF) whereby existing and new samples are computed to retrain a new model, incremental NMF (INMF) computes only the new samples to update the trained model incrementally, which can improve the computing efficiency. Considering the sparse characteristics of scattering centers in SAR images, we set the updating process under a generic sparse constraint (L p) for matrix decomposition of INMF. Thus, L p-INMF can extract sparse characteristics in SAR images. Experimental results using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data illustrate that the proposed L p-INMF method can not only update models with new samples more efficiently than conventional NMF, but also has a higher recognition rate than NMF and INMF.

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Section: Methodsmentioning

confidence: 99%

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

et al. 2018

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“…The resurgent development of many theoretical analysis frameworks [22,23] and effective algorithms [24] has been witnessed. The applications of the sparse signal representation technique mainly include radar imaging [25,26], image restoration [27], image classification [28,29], and pattern recognition [15,30]. The key of sparse signal model is based on the fact that a certain signal can be represented by an overcomplete basis set (dictionary).…”

Section: Srcmentioning

confidence: 99%

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

et al. 2018

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Abstract:The monogenic signal, which is defined as a linear combination of a signal and its Riesz-transformed one, provides a great opportunity for synthetic aperture radar (SAR) image recognition. However, the incredibly large number of components at different scales may result in too much of a burden for onboard computation. There is great information redundancy in monogenic signals because components at some scales are less discriminative or even have negative impact on classification. In addition, the heterogeneity of the three types of components will lower the quality of decision-making. To solve the problems above, a scale selection method, based on a weighted multi-task joint sparse representation, is proposed. A scale selection model is designed and the Fisher score is presented to measure the discriminative ability of components at each scale. The components with high Fisher scores are concatenated to three component-specific features, and an overcomplete dictionary is built. Meanwhile, the scale selection model produces the weight vector. The three component-specific features are then fed into a multi-task joint sparse representation classification framework. The final decision is made in terms of accumulated weighted reconstruction error. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset have proved the effectiveness and superiority of our method.

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“…However, JSRC restricts the interval of multiple views into a small range, which is difficult to realize in most real SAR scenarios. To improve this deficiency, multi-view SAR ATR with joint sparse representation over locally adaptive dictionary algorithm is proposed [37], [38], which can relax the restraint of JSRC to some extent. Nevertheless, it needs to update the locally adaptive dictionary online during testing stage, which is usually time-consuming and undesirable for practical applications.…”

Section: Introductionmentioning

confidence: 99%

Joint Supervised Dictionary and Classifier Learning for Multi-View SAR Image Classification

Ren

Zou

et al. 2019

IEEE Access

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A new multi-view sparse representation classification (SRC) algorithm based on joint supervised dictionary and classifier learning (MSRC-JSDC) is proposed for synthetic aperture radar (SAR) image classification. Unlike most existing sparse representation methods for SAR image classification, MSRC-JSDC learns a supervised sparse model from training samples by utilizing sample label information, rather than directly employs a predefined one. Moreover, a supervised classifier is jointly designed during dictionary learning, which can further promote the classification performance compared with unsupervised reconstruction based classifier. In the meantime, to enhance the representation capability of the sparse model, classification error is back propagated to the dictionary learning procedure to optimize dictionary atoms. In order to extract more recognition information from collected SAR images, a multi-view strategy is applied in testing stage. A new sparse constraint is introduced into multi-view sparse representation procedure so that both inner correlation and complementary information among multiple views can be extracted. This is helpful for alleviating the influence of SAR image's sensitivity on classification performance in such challenging scenarios as large depression variation and noise corruption. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate that the proposed method is more robust and performs better than some state-of-the-art approaches. INDEX TERMS Synthetic aperture radar (SAR), automatic target recognition (ATR), sparse representation based classification (SRC), dictionary learning.

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Automatic target recognition with joint sparse representation of heterogeneous multi-view SAR images over a locally adaptive dictionary

Cited by 42 publications

References 22 publications

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

Joint Supervised Dictionary and Classifier Learning for Multi-View SAR Image Classification

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