SAR Target Configuration Recognition Using Tensor Global and Local Discriminant Embedding

Huang, Xiayuan; Qiao, Hong; Zhang, Bo

doi:10.1109/lgrs.2015.2506659

Cited by 32 publications

(19 citation statements)

References 13 publications

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“…Those features are then used to produce a shared dictionary and a class dictionary. Specifically, the two learned dictionary can be obtained by solving the low-rank dictionary learning model in Equation (9). Given a query sample y, the monogenic feature of the query sample can be represented by χ(y).…”

Section: Implementation Of Target Recognitionmentioning

confidence: 99%

“…The traditional target recognition method, i.e., template-based strategy [8], is ineffective under EOCs, as slight changes in configuration or occlusions may give rise to significantly different scattering phenomenology, thus it is hard to quantify the similarity of targets between the templates and the query sample. To enhance the performance of target recognition, some feature-based methods are proposed to characterize SAR images, such as global or local structure descriptors [9][10][11], attributed scattering centers [12,13] and filer banks [3,14]. In recent years, the monogenic signal, which is a multidimensional analytic signal, is employed to characterize SAR images due to its rotation-invariance.…”

Section: Introductionmentioning

confidence: 99%

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SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal

Quan

Kuang

et al. 2019

Remote Sensing

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Synthetic aperture radar (SAR) target recognition under extended operating conditions (EOCs) is a challenging problem due to the complex application environment, especially for insufficient target variations and corrupted SAR images in the training samples. This paper proposes a new strategy to solve these problems for target recognition. The SAR images are firstly characterized by multi-scale components of monogenic signal. The generated monogenic features are decomposed to learn a class dictionary and a shared dictionary, which represent the possible intraclass variations information and the common information, respectively. Moreover, a sparse representation of the class dictionary and a dense representation of the shared dictionary are jointly employed to represent a query sample for classification. The validity of the proposed strategy is demonstrated with multiple comparative experiments on moving and stationary target acquisition and recognition (MSTAR) database.

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Section: Implementation Of Target Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal

Quan

Kuang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…SAR ATR algorithms that are based on machine learning methods can be further divided into two types, i.e., feature-based methods and deep learning methods. Feature-based methods [8,9] require features to be manually extracted from SAR images, while deep learning methods automatically extract features from SAR images. Thus, deep learning methods avoid the designing of feature extractors.…”

Section: Introductionmentioning

confidence: 99%

SAR ATR of Ground Vehicles Based on ESENet

2019

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In recent studies, synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms that are based on the convolutional neural network (CNN) have achieved high recognition rates in the moving and stationary target acquisition and recognition (MSTAR) dataset. However, in a SAR ATR task, the feature maps with little information automatically learned by CNN will disturb the classifier. We design a new enhanced squeeze and excitation (enhanced-SE) module to solve this problem, and then propose a new SAR ATR network, i.e., the enhanced squeeze and excitation network (ESENet). When compared to the available CNN structures that are designed for SAR ATR, the ESENet can extract more effective features from SAR images and obtain better generalization performance. In the MSTAR dataset containing pure targets, the proposed method achieves a recognition rate of 97.32% and it exceeds the available CNN-based SAR ATR algorithms. Additionally, it has shown robustness to large depression angle variation, configuration variants, and version variants.

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“…Thus, the main objective of feature extraction is to find a low-dimensional representation of a SAR image that could distinguishably represent the target. 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].…”

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 Configuration Recognition Using Tensor Global and Local Discriminant Embedding

Cited by 32 publications

References 13 publications

SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal

SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal

SAR ATR of Ground Vehicles Based on ESENet

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

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