R<sup>2</sup>FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images
Recently, convolutional neural network (CNN)-based methods have been extensively explored for ship detection in synthetic aperture radar (SAR) images due to their powerful feature representation abilities. However, there are still several obstacles hindering the development. First, ships appear in v...
Main Authors: | Shiqi Chen, Jun Zhang, Ronghui Zhan |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-06-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/12/12/2031 |
Similar Items
-
DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images
by: Kun Sun, et al.
Published: (2021-07-01) -
Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation
by: Shiqi Chen, et al.
Published: (2021-01-01) -
Refocusing Moving Ship Targets in SAR Images Based on Fast Minimum Entropy Phase Compensation
by: Xiangli Huang, et al.
Published: (2019-03-01) -
Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition
by: Yu-Huan Zhao, et al.
Published: (2018-09-01) -
Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images
by: Fei Gao, et al.
Published: (2020-08-01)