Articulation study for SAR ATR baseline algorithm

Paulson, Christopher; Nolan, Adam R.; Goley, Steve; Nehrbass, Stephen; Zelnio, Edmund G.

doi:10.1117/12.2523577

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…Along this track, one may consider a variety of onerous training conditions, where it is specifically measured how inaccuracies in the synthetic data generation process affects the ATR results on the measured data. One way to do this may be to purposely remove/modify details of the targets (e.g., remove the barrel of the T-72 tank) in the CAD modeling software and retrain the ATR models on such a tainted dataset [35]. Another interesting future work is to purposely modify details of the synthetic targets as a form of domain-relevant data augmentation, to potentially improve the performance of the ATR models through creation of a more diverse training dataset.…”

Section: Discussionmentioning

confidence: 99%

Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data

Inkawhich

Davis

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Obtaining measured Synthetic Aperture Radar (SAR) data for training Automatic Target Recognition (ATR) models can be too expensive (in terms of time and money) and complex of a process in many situations. In response, researchers have developed methods for creating synthetic SAR data for targets using electromagnetic prediction software, which is then used to enrich an existing measured training dataset. However, this approach relies on the availability of some amount of measured data. In this work, we focus on the case of having 100% synthetic training data, while testing on only measured data. We use the SAMPLE dataset public released by AFRL, and find significant challenges to learning generalizable representations from the synthetic data due to distributional differences between the two modalities and extremely limited training sample quantities. Using deep learning-based ATR models, we propose data augmentation, model construction, loss function choices, and ensembling techniques to enhance the representation learned from the synthetic data, and ultimately achieved over 95% accuracy on the SAMPLE dataset. We then analyze the functionality of our ATR models using saliency and feature-space investigations and find them to learn a more cohesive representation of the measured and synthetic data. Finally, we evaluate the out-oflibrary detection performance of our synthetic-only models and find that they are nearly 10% more effective than baseline methods at identifying measured test samples that do not belong to the training class set. Overall, our techniques and their compositions significantly enhance the feasibility of using ATR models trained exclusively on synthetic data.

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Section: Discussionmentioning

confidence: 99%

Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data

Inkawhich

Davis

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Feature-based algorithms are those with methods that run offline training supported exclusively by features extracted from the targets of interest. Among the methods employed by feature-based algorithms, we can highlight the following: Template Matching (TM) [ 5 , 6 , 7 , 11 , 30 , 37 ], Hidden Markov Model (HMM) [ 12 , 13 , 22 ], K-Nearest Neighbor (KNN) [ 27 , 28 ], Sparse Representation-based Classification (SRC) [ 8 , 29 ], Convolutional Neural Networks (CNN) [ 17 , 18 , 36 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], Support Vectors Machine (SVM) [ 9 ] and Gaussian Mixture Model (GMM) [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Synthetic data, also known as simulated data, are generated through computer simulations. The most common way to produce synthetic data is by using asymptotic electromagnetic scattering prediction codes with the support of Three-Dimensional Computer-Aided Design (3D-CAD) [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is an enormous demand for works that address the SAMPLE dataset [ 51 ]. So far, few works [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ] have been carried out that focus on the SAMPLE dataset for classifying measured data based exclusively on synthetic data, and they all use a feature-based approach with machine-learning algorithms like CNN or DenseNet.…”

Section: Introductionmentioning

confidence: 99%

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Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models

Araujo

Machado

Pettersson

2022

Sensors

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This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model represented by the set of scattering centers extracted from purely synthetic data, the proposed algorithm generates hypotheses for the set of scattering centers extracted from the target under test belonging to each class. A Goodness of Fit test is considered to verify each hypothesis, where the Likelihood Ratio Test is modified by a scattering center-weighting function common to both the model and target. Some algorithm variations are assessed for scattering center extraction and hypothesis generation and verification. The proposed solution is the first model-based classification algorithm to address the recently released Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset on a 100% synthetic training data basis. As a result, an accuracy of 91.30% in a 10-target test within a class experiment under Standard Operating Conditions (SOCs) was obtained. The algorithm was also pioneered in testing the SAMPLE dataset in Extend Operating Conditions (EOCs), assuming noise contamination and different target configurations. The proposed algorithm was shown to be robust for SNRs greater than −5 dB.

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“…Over the last few years, automatic target recognition using synthetic aperture radar (SAR ATR) has increasingly become important as a crucial means of surveillance [ 1 , 2 , 3 , 4 ]. SAR images can be obtained in most weather types, day and night, and at a high resolution [ 5 , 6 , 7 , 8 ]. Based on these characteristics, SAR ATR algorithms have been evaluated on several targets, including ground-based vehicles, aircrafts, and vessels, which are challenging for military operations [ 9 ].…”

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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Articulation study for SAR ATR baseline algorithm

Cited by 10 publications

References 15 publications

Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data

Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data

Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models

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

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