Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation
Abstract Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments....
Main Authors: | Hongjia Liu, Yubin Xiao, Xuan Wu, Yuanshu Li, Peng Zhao, Yanchun Liang, Liupu Wang, You Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2023-12-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-023-01294-y |
Similar Items
-
Enhancing Mask Transformer with Auxiliary Convolution Layers for Semantic Segmentation
by: Zhengyu Xia, et al.
Published: (2023-01-01) -
Semantic Segmentation Based on Depth Background Blur
by: Hao Li, et al.
Published: (2022-01-01) -
Semantic Segmentation for Aerial Mapping
by: Gabriel Martinez-Soltero, et al.
Published: (2020-08-01) -
Enhancing Semantically Masked Transformer With Local Attention for Semantic Segmentation
by: Zhengyu Xia, et al.
Published: (2023-01-01) -
A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation
by: Tongrui Zhang, et al.
Published: (2023-12-01)