Exploiting multi-level deep features via joint sparse representation with application to SAR target recognition

Lv, Junya

doi:10.1080/01431161.2019.1641246

Cited by 20 publications

(4 citation statements)

References 38 publications

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“… ESENet [ 33 ]: the ESENet is employed for SAR ATR. JSRDeep [ 36 ]: the CNN is developed for feature learning to generate multilayer feature maps. Afterwards, the joint sparse representation is employed to classify the deep feature vectors.…”

Section: Methodsmentioning

confidence: 99%

Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

Ding

2021

Computational Intelligence and Neuroscience

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A novel feature generation algorithm for the synthetic aperture radar image is designed in this study for automatic target recognition. As an adaptive 2D signal processing technique, bidimensional empirical mode decomposition is employed to generate multiscale bidimensional intrinsic mode functions from the original synthetic aperture radar images, which could better capture the broad spectral information and details of the target. And, the combination of the original image and decomposed bidimensional intrinsic mode functions could promisingly provide more discriminative information for correct target recognition. To reduce the high dimension of the original image as well as bidimensional intrinsic mode functions, multiset canonical correlations analysis is adopted to fuse them as a unified feature vector. The resultant feature vector highly reduces the high dimension while containing the inner correlations between the original image and decomposed bidimensional intrinsic mode functions, which could help improve the classification accuracy and efficiency. In the classification stage, the support vector machine is taken as the basic classifier to determine the target label of the test sample. In the experiments, the 10-class targets in the moving and stationary target acquisition and recognition dataset are classified to investigate the performance of the proposed method. Several operating conditions and reference methods are setup for comprehensive evaluation.

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Section: Methodsmentioning

confidence: 99%

Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

Ding

2021

Computational Intelligence and Neuroscience

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“…MVL and MTL have rapidly grown in ML and data mining in the past few years, which can obviously improve performance of model learning. In RSP domain, these related methods are popular in DL-based SAR-ATR, e.g., [325], [328], [331], [336], [337], [340], [343], [347]. Therefore, it is necessary to make a brief introduction about the review papers in MVL [36] and MTL [37].…”

Section: B Related Workmentioning

confidence: 99%

“…After finding an effective subset of training samples and constructing a new dictionary by multi-feature joint sparse representation learning as the first stage, the authors utilized multi-task collaborative representation to perform target images classification based on the new dictionary in second stage. A multi-level deep features-based multi-task learning algorithm was developed in [347] for SAR-ATR. This architecture employed joint sparse representation as the basic classifier and achieved an recognition rate of 99.38% on MSTAR under standard operating conditions (SOCs).…”

Section: A Sar Images Processingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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“…Based on the compressive sensing theory, SRC was first validated in face recognition [43] and further used in SAR ATR in many related works [40][41][42]. With the progress in deep learning, many novel networks were developed for SAR target recognition [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59], in which the convolutional neural network (CNN) is the mostly used. Network architectures including the all-convolutional neural networks (A-ConvNets) [46], enhanced squeeze and excitation network (ESENet) [47], gradually distilled CNN [48], cascade coupled CNN [49], and multistream CNN [50], were developed and applied.…”

Section: Introductionmentioning

confidence: 99%

Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

Liu

2020

International Journal of Optics

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This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition. The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes. Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors. This paper reconstructs the test sample based on local dictionaries formed by individual classes. Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure. Therefore, to solve the sparse coefficients, the BSBL is employed. The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance. Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.

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Exploiting multi-level deep features via joint sparse representation with application to SAR target recognition

Cited by 20 publications

References 38 publications

Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

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