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Ocean feature extraction from SAR Quicklook Imagery using Convolutional Neural Networks

Hashemi, M. and Rabus, B. and Lehner, Susanne (2018) Ocean feature extraction from SAR Quicklook Imagery using Convolutional Neural Networks. EUSAR 2018, 04.-07.Jun. 2018, Aachen, Deutschland.

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We used a large dataset of Sentinel-1 and TerraSAR-X quicklook images downloaded from the internet in order to classify the imagery into different classes of subscenes including: open ocean, land, sea ice and ships using Convolutional Neural Network (CNN) classifiers. We construct a training dataset of subscenes of the images using visual inspection and AIS data. We then focused on the open ocean scenes acquired under different environmental conditions to classify them into different wind speed and sea state categories. We compare the results to wind speed, sea state model results and NOAA buoy measurements. In order to find the subscenes containing ships, icebergs and oil slicks we further utilize the CNN over open ocean and coastal SAR scenes. Statistics on validation is given using categorical cross entropy loss. In addition several high resolution images are used in order to test the performance of the trained Convolutional Neural Network. This study will help to retrieve such images relevant to maritime investigations of ships, oil and environmental parameters using big data methods.

Item URL in elib:https://elib.dlr.de/117706/
Document Type:Conference or Workshop Item (Speech)
Title:Ocean feature extraction from SAR Quicklook Imagery using Convolutional Neural Networks
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Lehner, SusanneSusanne.Lehner (at) dlr.deUNSPECIFIED
Date:June 2018
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-5
Keywords:Synthetic Aperture Radar, Ocean Features, TerraSAR-X, Sentinel-1, Convolutional Neural Networks
Event Title:EUSAR 2018
Event Location:Aachen, Deutschland
Event Type:international Conference
Event Dates:04.-07.Jun. 2018
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 - SAR-Methodology
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:21 Dec 2017 16:00
Last Modified:31 Jul 2019 20:15

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