Exploring characteristics of neural network architecture computation for enabling SAR ATR

Melzer, Ryan; Severa, William; Plagge, Mark; Vineyard, Craig Michael

doi:10.1117/12.2588006

Cited by 8 publications

(4 citation statements)

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“…Jennison et al [ 47 ] achieved an accuracy of

although their synthetic data were transformed based on measured data, which in some ways can be considered a leak between test and training data. Finally, the best result obtained so far can be considered to be the one achieved by Melzer et al [ 48 ]. They tested 53 different neural networks achieving an average PCC of

with a CNN VGG HAS.…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…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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“…Jennison et al [ 47 ] achieved an accuracy of

with a CNN VGG HAS.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Araujo

Machado

Pettersson

2022

Sensors

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“…Among others, approaches have included the use of generative adversarial networks, 8,9 network saliency analysis, 10 adversarial training, 11,12 and investigating hyperparameter optimizations and a variety of image augmentations. 13,14 For any automated classification algorithm, there are a number of desirable properties, including how reliable the algorithm is, whether it is robust to variability in configuration, how it handles out-of-library confusers, and how it can effectively use synthetic data in its training process. By adding images from MSTAR that are not part of SAMPLE -both of different targets as well as different viewing geometries -we hope to provide the community with data and a challenge problem that can foster research in each of these areas.…”

Section: Introductionmentioning

confidence: 99%

SAMPLE with a side of MSTAR: extending SAMPLE with outliers and target variants from MSTAR

Lewis

Ashby

Zelnio

2023

Algorithms for Synthetic Aperture Radar Imagery XXX

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Deep learning is a technology applied to a host of problems in the decade since its introduction. Of particular interest for both defense and civil applications is the technology of automatic target recognition, which is a subset of visual detection and classification. However, these classification algorithms must be robust to out-of-library confusers and able to generalize across a variety of target types. In this paper, we augment the existing Synthetic and Measured Paired Labeled Experiment dataset of synthetic aperture radar images with the remainder of the public MSTAR dataset and define a set of experiments to encourage the development of traits beyond simple classification accuracy for target recognition algorithms.

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Multi-View Rotation Double-Layer Fusion CNN-LSTM for SAR Target Recognition

Wang,

Zhang,

Zhu

et al. 2024

2024 2nd International Conference on Algorithm, Image Processing and Machine Vision (AIPMV)

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Exploring characteristics of neural network architecture computation for enabling SAR ATR

Cited by 8 publications

References 0 publications

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

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

SAMPLE with a side of MSTAR: extending SAMPLE with outliers and target variants from MSTAR

Multi-View Rotation Double-Layer Fusion CNN-LSTM for SAR Target Recognition

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