<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Convolutional Neural Networks (CNN) have been widely utilized for Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) images. However, a large number of parameters and a huge training data requirements limit CNN’s use in SAR ATR. While previous works have primarily focused on model compression and structural modification of CNN, this paper employs the One-Vs-All (OVA) technique on CNN to address these issues. OVA-CNN comprises several Binary classifying CNNs (BCNNs) that act as an expert in correctly recognizing a single target. The BCNN that predicts the highest probability for a given target determines the class to which the target belongs. The evaluation of the model using various metrics on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset illustrates that the OVA-CNN has fewer weight parameters and training sample requirements while exhibiting a high recognition rate.</p>
</abstract>

Convolutional Neural Networks (CNN) have been widely utilized for Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) images. However, a large number of parameters and a huge training data requirements limit CNN’s use in SAR ATR. While previous works have primarily focused on model compression and structural modification of CNN, this paper employs the One-Vs-All (OVA) technique on CNN to address these issues. OVA-CNN comprises several Binary classifying CNNs (BCNNs) that act as an expert in correctly recognizing a single target. The BCNN that predicts the highest probability for a given target determines the class to which the target belongs. The evaluation of the model using various metrics on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset illustrates that the OVA-CNN has fewer weight parameters and training sample requirements while exhibiting a high recognition rate.

One-vs-All Convolutional Neural Networks for Synthetic Aperture Radar Target Recognition

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Convolutional Neural Networks (CNN) have been widely utilized for Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) images. However, a large...

One-vs-All Convolutional Neural Networks for Synthetic Aperture Radar Target Recognition

Online veröffentlicht: 22. Sept. 2022

Seitenbereich: 179 - 197

Eingereicht: 13. Dez. 2021

Akzeptiert: 02. Aug. 2022

DOI: https://doi.org/10.2478/cait-2022-0035

Schlüsselwörter
Automatic target recognition, synthetic aperture radar, convolutional neural network, deep learning, computer vision

© 2022 Bileesh Plakkal Babu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

One-vs-All Convolutional Neural Networks for Synthetic Aperture Radar Target Recognition

Online veröffentlicht: 22. Sept. 2022

Seitenbereich: 179 - 197

Eingereicht: 13. Dez. 2021

Akzeptiert: 02. Aug. 2022

DOI: https://doi.org/10.2478/cait-2022-0035

SchlüsselwörterAutomatic target recognition, synthetic aperture radar, convolutional neural network, deep learning, computer vision

© 2022 Bileesh Plakkal Babu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Automatic target recognition, synthetic aperture radar, convolutional neural network, deep learning, computer vision