SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm

Feng, Sijia; Ji, Kefeng; Zhang, Linbin; Ma, Xiaojie; Kuang, Gangyao

doi:10.1109/jstars.2021.3116979

Cited by 41 publications

(19 citation statements)

References 48 publications

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“…The proposed MMSR algorithm is tested on the standard MSTAR database (Feng et al., 2021; Inkawhich et al., 2021; Liu et al. (2018)), which was collected by the Sandia National Laboratory.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

Liu

Chen

2022

Radio Science

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A multi‐manifold based sparse representation (MMSR) algorithm which can preserve the local structure of the datasets in the reconstruction space with multiple descriptions is proposed for synthetic aperture radar (SAR) target configuration recognition. Different from the traditional manifold learning‐based algorithms that preserve the local structure of the datasets in the low‐dimensional space, the proposed MMSR guarantees local structure preserving of the datasets after feature extraction and sparse representation. The proposed MMSR can avoid corrupted relationships of the samples to ease the obstacle of target aspect angle sensitivity, which can realize more accurate SAR target configuration recognition. Satisfying results are obtained on the moving and stationary target acquisition and recognition database.

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“…The proposed MMSR algorithm is tested on the standard MSTAR database (Feng et al., 2021; Inkawhich et al., 2021; Liu et al. (2018)), which was collected by the Sandia National Laboratory.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

Liu

Chen

2022

Radio Science

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“…To exploit electromagnetic scattering characteristics of SAR targets, Wang et al [24] performed sub-band decomposition on complex-valued SAR images. Feng et al [25] decomposed a SAR target into multiple components according to its attribute scattering characteristics. The decomposed components are fed into a bidirectional network to extract the target's local features.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot SAR Target Recognition Based on Deep Kernel Learning

et al. 2022

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Deep learning methods have achieved state-of-the-art performance on synthetic aperture radar (SAR) target recognition tasks in recent years. However, obtaining sufficient SAR images for training these deep learning methods is costly in time and labor. This paper focuses on recognizing targets with a few training samples, that is, few-shot target recognition. We combine deep neural networks' powerful feature representation capabilities with the nonparametric flexibility of Gaussian processes (GPs) and propose a fewshot recognition model based on deep kernel learning. Deep neural networks map input samples into a lowdimensional embedding space. GPs employ a family of kernel functions to measure the similarity between embedded samples and classify them. During training, the model builds diverse related tasks to learn kernel functions with parameters shared across few-shot tasks. These learned kernel functions define common prior knowledge that can be transferred to unseen tasks. During testing, the model can recognize novel tasks with a few samples based on learned kernel functions. We conducted extensive experiments on a widely-used real SAR dataset to evaluate the model's effectiveness. The test results demonstrate that our model is superior to several recently proposed few-shot recognition methods.INDEX TERMS Synthetic aperture radar (SAR), target recognition, deep kernel learning, few-shot classification

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“…The first two steps are intended to extract potential target areas and remove false alarms [ 15 ]. The purpose of target classification is to automatically classify each input target image obtained by target detection and discrimination [ 16 ]. A large number of efforts have been made to achieve robust SAR ATR.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Targets in SAR Images Based on a WVV Feature Using a Subset of Scattering Centers

Lee

Kim

2022

Sensors

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This paper proposes a robust method for feature-based matching with potential for application to synthetic aperture radar (SAR) automatic target recognition (ATR). The scarcity of measured SAR data available for training classification algorithms leads to the replacement of such data with synthetic data. As attributed scattering centers (ASCs) extracted from the SAR image reflect the electromagnetic phenomenon of the SAR target, this is effective for classifying targets when purely synthetic SAR images are used as the template. In the classification stage, following preparation of the extracted template ASC dataset, some of the template ASCs were subsampled by the amplitude and the neighbor matching algorithm to focus on the related points of the test ASCs. Then, the subset of ASCs were reconstructed to the world view vector feature set, considering the point similarity and structure similarity simultaneously. Finally, the matching scores between the two sets were calculated using weighted bipartite graph matching and then combined with several weights for overall similarity. Experiments on synthetic and measured paired labeled experiment datasets, which are publicly available, were conducted to verify the effectiveness and robustness of the proposed method. The proposed method can be used in practical SAR ATR systems trained using simulated images.

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SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm

Cited by 41 publications

References 48 publications

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

Few-Shot SAR Target Recognition Based on Deep Kernel Learning

Recognition of Targets in SAR Images Based on a WVV Feature Using a Subset of Scattering Centers

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