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Deep neural networks based semantic segmentation for optical time series

Yao, Wei and Datcu, Mihai (2018) Deep neural networks based semantic segmentation for optical time series. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1-4. IGARSS 2018, 22-27. Juli 2018, Valencia, Spain.

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Abstract

Semantic segmentation or classification for satellite image time series (SITS) is a rarely touched topic, partly due to the difficulty in having the data, but more due to the unreachable task. In this research, we propose a dataset which consists of the Landsat image time series, with the purpose of performing multi-spectral semantic segmentation. As there is no ground truth information, we used unsupervised clustering to group time series into clusters, then Long short term memory (LSTM) unit based Recurrent neural networks (RNN) has been trained. We investigate the accuracy values for our test image patches, around 40% accuracy has been achieved for the sequence classification.

Item URL in elib:https://elib.dlr.de/120681/
Document Type:Conference or Workshop Item (Speech)
Title:Deep neural networks based semantic segmentation for optical time series
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Yao, WeiWei.Yao (at) dlr.deUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:July 2018
Journal or Publication Title:International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Page Range:pp. 1-4
Status:Published
Keywords:Satellite image time series, RNN, LSTM, Temporal pattern
Event Title:IGARSS 2018
Event Location:Valencia, Spain
Event Type:international Conference
Event Dates:22-27. Juli 2018
Organizer:IEEE, GRSS
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 - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Yao, Wei
Deposited On:29 Jun 2018 11:54
Last Modified:31 Jul 2019 20:18

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