SAR target recognition via incremental nonnegative matrix factorization with L&lt;inf&gt;p&lt;/inf&gt; sparse constraint

Dang, Sihang; Cui, Zongyong; Cao, Zongjie; Liu, Yuxiang; Min, Rui

doi:10.1109/radar.2017.7944260

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…Our previous study proposed a novel incremental nonnegative matrix factorization (INMF) and experiments were carried out with respect to recognition performance and efficiency in order to overcome the defects that conventional methods have in online processing. For details, please refer to [22].…”

Section: Incremental Nmf For Sar Target Recognitionmentioning

confidence: 99%

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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: Incremental Nmf For Sar Target Recognitionmentioning

confidence: 99%

“…where N is the number of samples. The definitions in Equations (22) and (23) refer to W * and H * , which are ground truth. The ground truth is derived from the direct matrix operation rather than the INMF iteration.…”

Section: Experiments On the Error Of The Decomposition Valuementioning

confidence: 99%

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

et al. 2018

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“…Yu et al (2014) advanced incremental graph regulated NMF (IGNMF) to achieve a better classification by maintaining the neighborhood distribution structure of the original highdimensional data during the process of dimension reduction. Dang et al (2017) integrated sparse constraints with incremental learning to devise an incremental NMF with L 1/2 sparse constraints (L 1/2 INMF) and successfully applied it to SAR image recognition. Wang and Sun (2017) introduced an incremental learning algorithm based on graph regularized NMF with sparseness constraints (IGNMFSC) and its effectiveness is verified by face recognition experiments.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal incremental non-negative matrix factorization algorithm and its application in image classification

Luo

Liu

2020

Comp. Appl. Math.

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To improve the sparseness of the base matrix in incremental non-negative matrix factorization, we in this paper present a new method, orthogonal incremental non-negative matrix factorization algorithm (OINMF), which combines the orthogonality constraint with incremental learning. OINMF adopts batch update in the process of incremental learning, and its iterative formulae are obtained using the gradient on the Stiefel manifold. The experiments on image classification show that the proposed method achieves much better sparseness and orthogonality, while retaining time efficiency of incremental learning.

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“…Different features have been explored to characterize the target signal [4,5], such as scattering centre features [6,7], filter bank features [3,8], pattern structure features [9,10] and statistical features [1,11]. Recently, low-rank matrix factorization (LMF), a particularly useful technique for data representation, has been developed for SAR ATR [12,13]. Specifically, LMF aims to find two matrices Z and H or more lower-dimensional matrices whose product provides a good low-dimensional approximation to the original matrix X such that X ≈ ZH.…”

Section: Introductionmentioning

confidence: 99%

“…Cui et al [12] proposed L 1/2 -norm regularized NMF for sparsely representing SAR images. Dang et al [13] used incremental NMF with an L p -norm constraint to further improve performance. Babaee et al [14] introduced two NMF variants, i.e., variance-constrained NMF and centre map NMF, to describe SAR images in an interactive system.…”

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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SAR target recognition via incremental nonnegative matrix factorization with L<inf>p</inf> sparse constraint

Cited by 5 publications

References 13 publications

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

Orthogonal incremental non-negative matrix factorization algorithm and its application in image classification

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

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