A self-supervised deep neural network (pretext & downstream) is devised for automatic target recognition (ATR) in synthetic aperture radar (SAR) images.
We found proposed network recognizes the targets with 98.90% accuracy (4-fold cross validated) with just one-third labelled dataset which makes it promising for ...
Request PDF | On Jul 16, 2023, Sai Kumar Bashetti and others published Self-Supervised Deep Network for Automatic Target Recognition in SAR | Find, ...
We proposed a knowledge-guided predictive architecture that incorporates SAR domain knowledge and mask image modeling. Our research highlights the importance of ...
The key aspect of SAR-JEPA is integrating SAR domain features to ensure high-quality self-supervised signals as target features. In addition, we employ local ...
Jan 13, 2023 · The purpose of this work is to establish the foundation for large-scale, open-field implementation of DL-based SAR-ATR systems.
In this paper, we introduce a new approach for learning from SAR images in the absence of abundant labeled SAR data. We demonstrate that our geometrically- ...
Missing: Network | Show results with:Network
Dec 9, 2024 · Nowadays, the developed deep neural networks (DNN) have been widely applied to synthetic aperture radar (SAR) image interpretation, ...
The purpose of this work is to establish the foundation for large-scale, open-field implementation of DL-based SAR-ATR systems, which is not only of great ...
Nov 26, 2023 · The key aspect of SAR-JEPA is integrating SAR domain features to ensure high-quality self-supervised signals as target features. Besides, we ...
Missing: Deep | Show results with:Deep