A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition

Author:

Inkawhich Nathan1ORCID

Affiliation:

1. Air Force Research Laboratory Information Directorate (AFRL/RI), High-Performance Systems Branch Rome, Rome, NY, USA

Funder

Air Force Research Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Few-Shot SAR Target Recognition via Enhanced Prototypical Network with Multiscale Region-Aware Convolution;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Efficient fine-grained automatic target recognition through active learning for defense applications;Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI;2024-06-07

3. Understanding Satellite Image Processing and Black Box Models with Ante-Hoc and Post-Hoc Explanations in Deep Learning;2024 10th International Conference on Communication and Signal Processing (ICCSP);2024-04-12

4. Active Multiview Fusion Framework for SAR Automatic Target Recognition;IEEE Transactions on Instrumentation and Measurement;2024

5. MSANet: Multiscale Self-Attention Aggregation Network for Few-Shot Aerial Imagery Segmentation;IEEE Transactions on Geoscience and Remote Sensing;2024

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