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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

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


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
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
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 ISI Web of Science:Yes
DOI :10.1109/TGRS.2018.2848901
Page Range:pp. 7147-7161
Publisher:IEEE - Institute of Electrical and Electronics Engineers
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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