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Target Classification in Oceanographic SAR Images with Deep Neural Networks: Architecture and Initial Results

Bentes da Silva, Carlos Augusto and Velotto, Domenico and Lehner, Susanne (2015) Target Classification in Oceanographic SAR Images with Deep Neural Networks: Architecture and Initial Results. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015, pp. 3703-3706. IEEE Xplore. IGARSS 2015, 26-31 July 2015, Milan, Italy. doi: 10.1109/IGARSS.2015.7326627. ISBN 978-1-4799-7929-5.

Full text not available from this repository.

Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7326627


Synthetic Aperture Radar (SAR) provides detailed information of Ocean's surface and man-made floating structures. Advances in the SAR technology and the deployment of new SAR satellites have contributed to an increasing number of remote sensing data available. Handle this large amount of data with human operators is infeasible. Therefore, the use of automated tools to process remote sensing images, identify regions of interest, and select relevant information are needed. The use of neural networks to solve SAR image classification problems is well known. The typical architecture consists of a shallow feed-forward neural network with an input layer, a hidden layer, and an output layer. This type of neural network combined with back-propagation training algorithm is able to solve complex problems in SAR image analysis. However, this architecture is unable to take advantage of unlabeled data during its training process, and in many cases the input features need to be carefully tuned in order to reduce the overall network complexity. This paper proposes the application of Deep Neural Networks (DNN) to perform oceanographic-object classification.

Item URL in elib:https://elib.dlr.de/95526/
Document Type:Conference or Workshop Item (Speech)
Additional Information:published online; http://www.igarss2015.org/
Title:Target Classification in Oceanographic SAR Images with Deep Neural Networks: Architecture and Initial Results
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Velotto, DomenicoUNSPECIFIEDhttps://orcid.org/0000-0002-8592-0652UNSPECIFIED
Journal or Publication Title:Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 3703-3706
Publisher:IEEE Xplore
Keywords:SAR Oceanography, Machine Learning, Deep Neural Networks, Automatic Target Identification
Event Title:IGARSS 2015
Event Location:Milan, Italy
Event Type:international Conference
Event Dates:26-31 July 2015
Organizer:IEEE Geoscience and Remote Sensing Society
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 Entwicklung und Erprobung von Verfahren zur Gewässerfernerkundung (old)
Location: Bremen , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Remote Sensing Technology Institute
Deposited By: Kaps, Ruth
Deposited On:13 Apr 2015 15:58
Last Modified:29 Mar 2023 00:22

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