A DEEP LEARNING APPROACH TO SHIP DETECTION AND CHARACTERIZATION FROM MULTIRESOLUTION SATELLITE SAR IMAGES

被引：1

作者：

Povoli, Sergio ^{[1
]}

Di Donna, Mauro ^{[2
]}

Macina, Flavia ^{[2
]}

Avolio, Corrado ^{[2
]}

Zavagli, Massimo ^{[2
]}

Costantini, Mario ^{[1
]}

Bruzzone, Lorenzo ^{[1
]}

机构：

[1] Univ Trento, Dept Informat Engn & Comp Sci, Trento, Italy

[2] E GEOS, Rome, Italy

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

关键词：

Ship detection; SAR; ship characterization; Oriented YOLOv3;

D O I：

10.1109/IGARSS46834.2022.9883167

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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.

引用

页码：643 / 646

页数：4