TY - GEN
T1 - Rotation Awareness Based Self-Supervised Learning for SAR Target Recognition
AU - Zhang, Shuai
AU - Wen, Zaidao
AU - Liu, Zhunga
AU - Pan, Quan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In this paper, we newly suggest that more attention should be paid on learning rotation-equivariant and label-invariant features for each target instead of the conventional rotation-invariant ones. To achieve this goal, we present a novel rotation awareness based self-supervised learning (RR-SSL) deep model to recognize the behavior of target rotation, which is also benefit from the discriminative training scheme without manual labeling. Then this model is incorporated into another deep discriminative model of target recognition to form a dual-task learning framework, where their bottom layers are shared to capture the expected features. Sufficient experimental results on moving and stationary target acquisition and recognition (MSTAR) database demonstrate the effectiveness of our proposed model. The overall framework can achieve a better or comparative recognition accuracy compared with other state-of-the-art SAR-ATR algorithms.
AB - In this paper, we newly suggest that more attention should be paid on learning rotation-equivariant and label-invariant features for each target instead of the conventional rotation-invariant ones. To achieve this goal, we present a novel rotation awareness based self-supervised learning (RR-SSL) deep model to recognize the behavior of target rotation, which is also benefit from the discriminative training scheme without manual labeling. Then this model is incorporated into another deep discriminative model of target recognition to form a dual-task learning framework, where their bottom layers are shared to capture the expected features. Sufficient experimental results on moving and stationary target acquisition and recognition (MSTAR) database demonstrate the effectiveness of our proposed model. The overall framework can achieve a better or comparative recognition accuracy compared with other state-of-the-art SAR-ATR algorithms.
KW - automatic target recognition
KW - equivariant feature learning
KW - rotation awareness
KW - self-supervised learning
KW - synthetic aperture radar
UR - http://www.scopus.com/inward/record.url?scp=85077693730&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8899169
DO - 10.1109/IGARSS.2019.8899169
M3 - 会议稿件
AN - SCOPUS:85077693730
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1378
EP - 1381
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -