Abstract
Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) images has received a lot of attention in the past two decades. The prevailing assumption in most of the classification studies is the 'closed set' modeling. However, the system might having to operate in an open set environment, in which unknown targets may be given to the system for classification. To tackle this problem, the Openmax classifier has been recently introduced in optical domain to enable convolutional neural networks (CNNs) to distinguish between open set and closed set classes. To the best knowledge of the authors, Openmax has not been yet examined in the context of SAR or ISAR images. In this work, we address the open set recognition problem in the radar domain. We evaluate the performance of the Openmax classifier using real images from the SAMPLE dataset, which is a subset of the well-known MSTAR dataset. A special emphasis has been given to the tail-fitting procedure that plays a major role in the Openmax scores calculation. Moreover, the conventional performance indexes under different global thresholds are also analyzed.
Original language | English |
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Title of host publication | 2022 IEEE Radar Conference (RadarConf22) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 6 |
ISBN (Electronic) | 9781728153681 |
ISBN (Print) | 9781728153698 (PoD) |
DOIs | |
Publication status | Published - 3 May 2022 |
Event | 2022 IEEE Radar Conference, RadarConf 2022 - New York City, United States Duration: 21 Mar 2022 → 25 Mar 2022 |
Publication series
Name | Proceedings of the IEEE Radar Conference |
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Publisher | IEEE |
ISSN (Print) | 1097-5764 |
ISSN (Electronic) | 2640-7736 |
Conference
Conference | 2022 IEEE Radar Conference, RadarConf 2022 |
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Country/Territory | United States |
City | New York City |
Period | 21/03/22 → 25/03/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Convolutional Neural Network
- Deep learning
- MSTAR dataset
- Open Set Recognition
ASJC Scopus subject areas
- Computer Networks and Communications
- Signal Processing
- Instrumentation