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HSF-Net: Multiscale Deep Feature Embedding for Ship Detection in Optical Remote Sensing Imagery

Li, Qingpeng and Mou, Lichao and Liu, Qingjie and Wang, Yunhong and Zhu, Xiao Xiang (2018) HSF-Net: Multiscale Deep Feature Embedding for Ship Detection in Optical Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, 56 (12), pp. 7147-7161. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2018.2848901 ISSN 0196-2892

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/8412136

Abstract

Ship detection is an important and challenging task in remote sensing applications. Most methods utilize specially designed hand-crafted features to detect ships, and they usually work well only on one scale, which lack generalization and impractical to identify ships with various scales from multiresolu- tion images. In this paper, we propose a novel deep feature-based method to detect ships in very high-resolution optical remote sensing images. In our method, a regional proposal network is used to generate ship candidates from feature maps produced by a deep convolutional neural network. To efficiently detect ships with various scales, a hierarchical selective filtering layer is proposed to map features in different scales to the same scale space. The proposed method is an end-to-end network that can detect both inshore and offshore ships ranging from dozens of pixels to thousands. We test our network on a large ship data set which will be released in the future, consisting of Google Earth images, GaoFen-2 images, and unmanned aerial vehicle data. Experiments demonstrate high precision and robustness of our method. Further experiments on aerial images show its good generalization to unseen scenes.

Item URL in elib:https://elib.dlr.de/122448/
Document Type:Article
Title:HSF-Net: Multiscale Deep Feature Embedding for Ship Detection in Optical Remote Sensing Imagery
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Li, QingpengBeihang University, BeijingUNSPECIFIED
Mou, Lichaolichao.mou (at) dlr.deUNSPECIFIED
Liu, QingjieBeihang University, BeijingUNSPECIFIED
Wang, YunhongBeihang University, BeijingUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Date:December 2018
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:56
DOI :10.1109/TGRS.2018.2848901
Page Range:pp. 7147-7161
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Marine vehicles, Feature extraction, Remote sensing, Object detection, Proposals, Optical sensors, Optical imaging, Convolutional neural network (CNN), multiscale analysis, object detection, optical image, remote sensing.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hoffmann, Eike Jens
Deposited On:23 Oct 2018 14:36
Last Modified:10 Dec 2018 13:05

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