A Deep Learning Approach to Ship Detection and Characterization from Multiresolution Satellite SAR Images
- Resource Type
- Conference
- Authors
- Povoli, Sergio; Di Donna, Mauro; Macina, Flavia; Avolio, Corrado; Zavagli, Massimo; Costantini, Mario; Bruzzone, Lorenzo
- Source
- IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International. :643-646 Jul, 2022
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Deep learning
Satellites
Parameter estimation
Surveillance
Geoscience and remote sensing
Object detection
Radar polarimetry
Ship detection
SAR
ship characterization
Oriented YOLOv3
- Language
- ISSN
- 2153-7003
Ship detection using synthetic aperture radar images is a key technology in maritime surveillance applications. In addition to the position of the vessel, the characterization of the target (length, width and orientation) is often a requirement. In this paper, we present a deep learning architecture for object detection we developed by modifying the popular YOLOv3 architecture to apply to vessel detection and parameter estimation from SAR images. The proposed architecture was trained and tested on a large dataset of SAR images defined in this work. It contains images covering a wide range of spatial resolutions (pixel spacing ranging from 1.5m to 50m) and labelled with oriented bounding boxes to associate to each vessel not only its position but also size and orientation. The obtained results are very promising and confirm the validity of the approach.