SAR Ship Detection with Deep Land Detection Networks and Land Masking
- Resource Type
- Conference
- Authors
- Kim, Byoungjun; Yoo, Minjung; Kim, Sunok
- Source
- 2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) Consumer Electronics-Asia (ICCE-Asia), 2022 IEEE International Conference on. :1-3 Oct, 2022
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Deep learning
Detectors
Apertures
Feature extraction
Radar polarimetry
Marine vehicles
Synthetic Aperture Radar
Land detection
Land mask
SAR Ship detection
- Language
We propose a new Synthetic Aperture Radar (SAR) ship detection framework using deep learning that makes SAR land mask to improve SAR ship detection performance. To overcome the disadvantage of a small number of SAR dataset, the land detection networks take a large amount of small image patches as input and effectively learn land feature for detecting land mask. We then eliminate land using the detected land mask and apply artificial noise to preserve image distribution. With these schemes, SAR ship detector can concentrate on ship feature rather than land feature. Experimental results demonstrated that the proposed method outperforms baseline.