Improving Landslide Detection on SAR Data Through Deep Learning

In this letter, we use deep learning convolutional neural networks (CNNs) to compare the landslide mapping and classification performances of optical images (from Sentinel-2) and synthetic aperture radar (SAR) images (from Sentinel-1). The training, validation, and test zones used to independently e...

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Detalles Bibliográficos
Autores: Nava, L, Monserrat, O, Catani, F
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p4123
Acceso en línea:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=4123
Access Level:acceso abierto
Palabra clave:Terrain factors
Synthetic aperture radar
Optical sensors
Optical imaging
Training
Deep learning
Satellites
Landslides
convolutional neural networks (CNNs)
deep learning (DL)
image classification
landslide detection
remote sensing (RS)
Sentinel-1
Sentinel-2
synthetic aperture radar (SAR)
TensorFlow
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spelling Improving Landslide Detection on SAR Data Through Deep LearningNava, LMonserrat, OCatani, FTerrain factorsSynthetic aperture radarOptical sensorsOptical imagingTrainingDeep learningSatellitesLandslidesconvolutional neural networks (CNNs)deep learning (DL)image classificationlandslide detectionremote sensing (RS)Sentinel-1Sentinel-2synthetic aperture radar (SAR)TensorFlowIn this letter, we use deep learning convolutional neural networks (CNNs) to compare the landslide mapping and classification performances of optical images (from Sentinel-2) and synthetic aperture radar (SAR) images (from Sentinel-1). The training, validation, and test zones used to independently evaluate the performance of the CNN on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multipolarization SAR as well as optical data by means of a CNN implemented in TensorFlow that points out the locations where the landslide class is predicted as more likely. As expected, the CNN runs on optical images proved itself excellent for the landslide detection task, achieving an overall accuracy of 98.96%, while CNNs based on the combination of ground range detected (GRD) SAR data reached overall accuracies beyond 95%. Our findings show that the integrated use of SAR data may also allow for rapid detection even during storms and under dense cloud cover and provides comparable accuracy to classical optical change detection in landslide recognition and detection.Institute of Electrical and Electronics Engineers Inc.2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=4123IEEE GEOSCIENCE AND REMOTE SENSING LETTERSISSN: 1545598XISSNe: 15580571reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/openAccessoai:cttc.fundanetsuite.com:p41232026-06-17T11:44:47Z
dc.title.none.fl_str_mv Improving Landslide Detection on SAR Data Through Deep Learning
title Improving Landslide Detection on SAR Data Through Deep Learning
spellingShingle Improving Landslide Detection on SAR Data Through Deep Learning
Nava, L
Terrain factors
Synthetic aperture radar
Optical sensors
Optical imaging
Training
Deep learning
Satellites
Landslides
convolutional neural networks (CNNs)
deep learning (DL)
image classification
landslide detection
remote sensing (RS)
Sentinel-1
Sentinel-2
synthetic aperture radar (SAR)
TensorFlow
title_short Improving Landslide Detection on SAR Data Through Deep Learning
title_full Improving Landslide Detection on SAR Data Through Deep Learning
title_fullStr Improving Landslide Detection on SAR Data Through Deep Learning
title_full_unstemmed Improving Landslide Detection on SAR Data Through Deep Learning
title_sort Improving Landslide Detection on SAR Data Through Deep Learning
dc.creator.none.fl_str_mv Nava, L
Monserrat, O
Catani, F
author Nava, L
author_facet Nava, L
Monserrat, O
Catani, F
author_role author
author2 Monserrat, O
Catani, F
author2_role author
author
dc.subject.none.fl_str_mv Terrain factors
Synthetic aperture radar
Optical sensors
Optical imaging
Training
Deep learning
Satellites
Landslides
convolutional neural networks (CNNs)
deep learning (DL)
image classification
landslide detection
remote sensing (RS)
Sentinel-1
Sentinel-2
synthetic aperture radar (SAR)
TensorFlow
topic Terrain factors
Synthetic aperture radar
Optical sensors
Optical imaging
Training
Deep learning
Satellites
Landslides
convolutional neural networks (CNNs)
deep learning (DL)
image classification
landslide detection
remote sensing (RS)
Sentinel-1
Sentinel-2
synthetic aperture radar (SAR)
TensorFlow
description In this letter, we use deep learning convolutional neural networks (CNNs) to compare the landslide mapping and classification performances of optical images (from Sentinel-2) and synthetic aperture radar (SAR) images (from Sentinel-1). The training, validation, and test zones used to independently evaluate the performance of the CNN on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multipolarization SAR as well as optical data by means of a CNN implemented in TensorFlow that points out the locations where the landslide class is predicted as more likely. As expected, the CNN runs on optical images proved itself excellent for the landslide detection task, achieving an overall accuracy of 98.96%, while CNNs based on the combination of ground range detected (GRD) SAR data reached overall accuracies beyond 95%. Our findings show that the integrated use of SAR data may also allow for rapid detection even during storms and under dense cloud cover and provides comparable accuracy to classical optical change detection in landslide recognition and detection.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=4123
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=4123
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN: 1545598X
ISSNe: 15580571
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
collection r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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