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Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

Mou, Lichao and Zhu, Xiaoxiang and Vakalopoulou, Maria and Karantzalos, Konstantinos and Paragios, Nikos and Le Saux, Bertrand and Moser, Gabriele and Tuia, Devis (2017) Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (8), pp. 3435-3447. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2017.2696823. ISSN 1939-1404.

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Official URL: http://ieeexplore.ieee.org/document/7948767/

Abstract

In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 images and a video captured by the Iris camera on-board the International Space Station. The problems addressed and the techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and mixed ideas and methodologies from the remote sensing, video processing, and computer vision. In particular, the winning team developed a deep learning method to jointly address spatial scene labeling and temporal activity modeling using the available image and video data. The second place team proposed a random field model to simultaneously perform coregistration of multitemporal data, semantic segmentation, and change detection. The methodological key ideas of both these approaches and the main results of the corresponding experimental validation are discussed in this paper.

Item URL in elib:https://elib.dlr.de/119432/
Document Type:Article
Title:Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Mou, Lichaolichao.mou (at) dlr.deUNSPECIFIED
Zhu, Xiaoxiangxiao.zhu (at) dlr.deUNSPECIFIED
Vakalopoulou, Mariamariavak (at) central.ntua.grUNSPECIFIED
Karantzalos, Konstantinoskarank (at) central.ntua.grUNSPECIFIED
Paragios, Nikosnikos.paragios (at) ecp.frUNSPECIFIED
Le Saux, Bertrandbertrand.le_saux (at) onera.frUNSPECIFIED
Moser, Gabrielegabriele.moser (at) unige.itUNSPECIFIED
Tuia, Devisdevis.tuia (at) wur.nlUNSPECIFIED
Date:2017
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.1109/JSTARS.2017.2696823
Page Range:pp. 3435-3447
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Change detection, convolutional neural networks (CNN), deep learning, image analysis and data fusion, multiresolution, multisource, multimodal, random fields, tracking, video from space.
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)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Mou, LiChao
Deposited On:21 Mar 2018 12:27
Last Modified:24 Nov 2020 04:13

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