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Anomaly Detection in Post Fire Assessment

Coca, Mihai and Datcu, Mihai (2021) Anomaly Detection in Post Fire Assessment. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 8620-8623. Institute of Electrical and Electronics Engineers. IGARSS 2021, 11-16 July 2021, Brussels, Belgium. doi: 10.1109/IGARSS47720.2021.9554169. ISBN 978-1-6654-0369-6. ISSN 2153-7003.

[img] PDF - Only accessible within DLR bis June 2022

Official URL: https://ieeexplore.ieee.org/document/9554169


Over the last few years, natural disasters elevated dangerously in terms of immensity and prevalence over areas covered by forest and urban woodlands. Fast-spreading nature of the wildfires determine quick uncontrollable situations' causing significant effects in short periods. Despite increased difficulty in image processing approaches due to temporal resolution, complexity of spectral bands and illumination conditions, imagery data streams available from sun-synchronous satellites provide geospatial intelligence in monitoring and preventing fire threats. In this paper, we proposed a local scale burned area estimation framework that employs multispectral images in a deep learning architecture for detecting burned surfaces at patch level. This goal is accomplished by using an autoencoder (AE) network in which the latent feature layer learns normal background distribution, beneficial to background reconstruction. Furthermore, an outlier detection method (OCSVM) is used with aggregated features, latent and covariance components, in order to estimate burned coverage. Our method operates on data retrieved from Sentinel-2 (S2) constellation streaming source, which mainly contain normal scenes and limited fire affected spots.

Item URL in elib:https://elib.dlr.de/144962/
Document Type:Conference or Workshop Item (Speech)
Title:Anomaly Detection in Post Fire Assessment
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Coca, MihaiUniversity Politehnica of Bucharest, RomaniaUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:July 2021
Journal or Publication Title:International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/IGARSS47720.2021.9554169
Page Range:pp. 8620-8623
Publisher:Institute of Electrical and Electronics Engineers
Keywords:Deep Learning, Anomaly Detection,Wildfires, OCSVM, Burned Area Estimation, Sentinel-2
Event Title:IGARSS 2021
Event Location:Brussels, Belgium
Event Type:international Conference
Event Dates:11-16 July 2021
Organizer:Institute of Electrical and Electronics Engineers
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 - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Otgonbaatar, Soronzonbold
Deposited On:18 Nov 2021 12:25
Last Modified:18 Nov 2021 12:25

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