TY - GEN
T1 - Land-Sea Clutter Classification for Over-the-Horizon Radar via Dual Attention Aided Residual Neural Networks
AU - Li, Can
AU - Pan, Quan
AU - Zhang, Zuowei
AU - Liu, Zhunga
AU - Bai, Xianglong
AU - Pan, Kunpeng
N1 - Publisher Copyright:
© 2024 ISIF.
PY - 2024
Y1 - 2024
N2 - Deep learning has been widely used in the field of radar image classification because of its powerful feature extraction capabilities. In the land-sea clutter classification of sky-wave over-the-horizon radar (OTHR), deep learning methods perform poorly due to the radar receiver noise and the ionosphere. Addressing this challenge, a dual attention aided residual neural networks (DAAResNet) is proposed for OTHR land-sea classification. Leveraging prior knowledge that landsea clutter features predominantly cluster around the 0 Hz frequency, two attention mechanisms are introduced. Firstly, a channel attention module (CAM) is proposed, which directs the network's focus towards critical channels. Secondly, a frequency attention module (FAM) is proposed, which directs attention towards pivotal frequencies. The classification performance of DAAResNet is validated on the original dataset and the scarce dataset. Experimental results show that DAAResNet outperforms state-of-the-art methods.
AB - Deep learning has been widely used in the field of radar image classification because of its powerful feature extraction capabilities. In the land-sea clutter classification of sky-wave over-the-horizon radar (OTHR), deep learning methods perform poorly due to the radar receiver noise and the ionosphere. Addressing this challenge, a dual attention aided residual neural networks (DAAResNet) is proposed for OTHR land-sea classification. Leveraging prior knowledge that landsea clutter features predominantly cluster around the 0 Hz frequency, two attention mechanisms are introduced. Firstly, a channel attention module (CAM) is proposed, which directs the network's focus towards critical channels. Secondly, a frequency attention module (FAM) is proposed, which directs attention towards pivotal frequencies. The classification performance of DAAResNet is validated on the original dataset and the scarce dataset. Experimental results show that DAAResNet outperforms state-of-the-art methods.
KW - channel attention module
KW - Clutter classification
KW - frequency attention module
KW - over-the-horizon radar (OTHR)
UR - http://www.scopus.com/inward/record.url?scp=85207691274&partnerID=8YFLogxK
U2 - 10.23919/FUSION59988.2024.10706360
DO - 10.23919/FUSION59988.2024.10706360
M3 - 会议稿件
AN - SCOPUS:85207691274
T3 - FUSION 2024 - 27th International Conference on Information Fusion
BT - FUSION 2024 - 27th International Conference on Information Fusion
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th International Conference on Information Fusion, FUSION 2024
Y2 - 7 July 2024 through 11 July 2024
ER -