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Ship Classification in TerraSAR-X Images with Convolutional Neural Networks

Bentes da Silva, Carlos Augusto and Velotto, Domenico and Tings, Björn (2017) Ship Classification in TerraSAR-X Images with Convolutional Neural Networks. IEEE Journal of Oceanic Engineering, 43 (1), pp. 258-266. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JOE.2017.2767106. ISSN 0364-9059.

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Official URL: https://doi.org/10.1109/JOE.2017.2767106


Synthetic aperture radar (SAR) is an important instrument for oceanographic observations, providing detailed information of oceans`surface and artificial floating structures. Due to advances in SAR technology and deployment of new SAR satellites, an increasing amount of data is available, and the development of efficient classification systems based on deep learning is possible. A deep neural network has improved the state of the art in classification tasks of optical images, but its use in SAR classification problems has been less exploited. In this paper, a full workflow for SAR maritime targets detection and classification on TerraSAR-X high-resolution image is presented, and convolutional neural networks (CNNs) recently proposed in the literature are cross evaluated on a common data set composed of five maritime classes, namely, cargo, tanker, windmill, platform, and harbor structure. Based on experiments and tests, a multiple input resolution CNN model is proposed and its performance is evaluated. Our results indicate that CNNs are efficient models to perform maritime target classification in SAR images, and the combination of different input resolutions in the CNN model improves its ability to derive features, increasing the overall classification score.

Item URL in elib:https://elib.dlr.de/97930/
Document Type:Article
Additional Information:corresponding author: Carlos Bentes
Title:Ship Classification in TerraSAR-X Images with Convolutional Neural Networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Bentes da Silva, Carlos AugustoCarlos.Bentes (at) tum.dehttps://orcid.org/0000-0002-5941-334X
Velotto, DomenicoDomenico.Velotto (at) dlr.dehttps://orcid.org/0000-0002-8592-0652
Tings, BjörnBjoern.Tings (at) dlr.dehttps://orcid.org/0000-0002-1945-6433
Date:16 November 2017
Journal or Publication Title:IEEE Journal of Oceanic Engineering
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/JOE.2017.2767106
Page Range:pp. 258-266
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:Special Issue Marine and Maritime Radar Remote Sensing
Keywords:SAR, ship classification, deep learning, convolutional neural networks
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old), R - SAR methods
Location: Bremen , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Kaps, Ruth
Deposited On:30 Nov 2017 16:49
Last Modified:06 Sep 2019 15:20

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