Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping
Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well-suited to detect weak signatures of certain disturbances of ecological systems...
Published in: | Canadian Journal of Remote Sensing |
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Taylor & Francis
2022
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Online Access: | https://hdl.handle.net/10037/27111 https://doi.org/10.1080/07038992.2022.2135497 |
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ftunivtroemsoe:oai:munin.uit.no:10037/27111 2023-05-15T18:33:40+02:00 Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping Vers une détection ciblée de changements à l’aide d’images de télédétection hétérogènes pour la cartographie de la mortalité sylvestre Agersborg, Jørgen Andreas Luppino, Luigi Tommaso Anfinsen, Stian Normann Jepsen, Jane Uhd 2022-10-20 https://hdl.handle.net/10037/27111 https://doi.org/10.1080/07038992.2022.2135497 eng eng Taylor & Francis Canadian Journal of Remote Sensing (CJRS) https://doi.org/10.1080/07038992.2022.2135497 Agersborg JAA, Luppino LT, Anfinsen SN, Jepsen JU. Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping. Canadian Journal of Remote Sensing (CJRS). 2022:1-23 FRIDAID 2063566 doi:10.1080/07038992.2022.2135497 0703-8992 1712-7971 https://hdl.handle.net/10037/27111 Attribution 4.0 International (CC BY 4.0) openAccess Copyright 2022 The Author(s) https://creativecommons.org/licenses/by/4.0 CC-BY VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Annen informasjonsteknologi: 559 VDP::Technology: 500::Information and communication technology: 550::Other information technology: 559 Endringsdeteksjon / Change detection Jordobservasjon fra satellitter / Earth-monitoring satellites Satellittfjernmåling / Remote sensing Skogøkologi / Forest ecology Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2022 ftunivtroemsoe https://doi.org/10.1080/07038992.2022.2135497 2022-10-26T23:01:05Z Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well-suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and one-class classification (OCC). We aim to map forest mortality caused by an outbreak of geometrid moths in a sparsely forested forest-tundra ecotone using multisource satellite images. The images preceding and following the event are collected by Landsat-5 and RADARSAT-2, respectively. Using a recent deep learning method for change-aware image translation, we compute difference images in both satellites’ respective domains. These differences are stacked with the original pre- and post-event images and passed to an OCC trained on a small sample from the targeted change class. The classifier produces a credible map of the complex pattern of forest mortality. Plusieurs méthodes génériques de détection de changements à partir d’images satellites issues de sources hétérogènes (radar à synthèse d’ouverture, optique, etc.) ont été développées récemment. Cependant, celles-ci sont rarement adaptées à la détection des signatures spectrales peu distinctives de certaines perturbations des systèmes écologiques. Pour remédier à ce problème, nous proposons une nouvelle approche basée sur le transfert d’image et un algorithme de classification à une classe (OCC). Notre objectif est de cartographier l’effet d’une épidémie de papillons géométrides dans un écotone forêt-toundra peu boisé en utilisant des images satellites multisources. Les images précédant et suivant l’événement proviennent de Landsat-5 et RADARSAT-2, respectivement. En utilisant une méthode récente d’apprentissage profond de transfert d’images sensible aux changements, nous calculons les images de différence dans les domaines respectifs des deux satellites. Ces images de ... Article in Journal/Newspaper toundra Tundra University of Tromsø: Munin Open Research Archive Canadian Journal of Remote Sensing 1 23 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Annen informasjonsteknologi: 559 VDP::Technology: 500::Information and communication technology: 550::Other information technology: 559 Endringsdeteksjon / Change detection Jordobservasjon fra satellitter / Earth-monitoring satellites Satellittfjernmåling / Remote sensing Skogøkologi / Forest ecology |
spellingShingle |
VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Annen informasjonsteknologi: 559 VDP::Technology: 500::Information and communication technology: 550::Other information technology: 559 Endringsdeteksjon / Change detection Jordobservasjon fra satellitter / Earth-monitoring satellites Satellittfjernmåling / Remote sensing Skogøkologi / Forest ecology Agersborg, Jørgen Andreas Luppino, Luigi Tommaso Anfinsen, Stian Normann Jepsen, Jane Uhd Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping |
topic_facet |
VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Annen informasjonsteknologi: 559 VDP::Technology: 500::Information and communication technology: 550::Other information technology: 559 Endringsdeteksjon / Change detection Jordobservasjon fra satellitter / Earth-monitoring satellites Satellittfjernmåling / Remote sensing Skogøkologi / Forest ecology |
description |
Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well-suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and one-class classification (OCC). We aim to map forest mortality caused by an outbreak of geometrid moths in a sparsely forested forest-tundra ecotone using multisource satellite images. The images preceding and following the event are collected by Landsat-5 and RADARSAT-2, respectively. Using a recent deep learning method for change-aware image translation, we compute difference images in both satellites’ respective domains. These differences are stacked with the original pre- and post-event images and passed to an OCC trained on a small sample from the targeted change class. The classifier produces a credible map of the complex pattern of forest mortality. Plusieurs méthodes génériques de détection de changements à partir d’images satellites issues de sources hétérogènes (radar à synthèse d’ouverture, optique, etc.) ont été développées récemment. Cependant, celles-ci sont rarement adaptées à la détection des signatures spectrales peu distinctives de certaines perturbations des systèmes écologiques. Pour remédier à ce problème, nous proposons une nouvelle approche basée sur le transfert d’image et un algorithme de classification à une classe (OCC). Notre objectif est de cartographier l’effet d’une épidémie de papillons géométrides dans un écotone forêt-toundra peu boisé en utilisant des images satellites multisources. Les images précédant et suivant l’événement proviennent de Landsat-5 et RADARSAT-2, respectivement. En utilisant une méthode récente d’apprentissage profond de transfert d’images sensible aux changements, nous calculons les images de différence dans les domaines respectifs des deux satellites. Ces images de ... |
format |
Article in Journal/Newspaper |
author |
Agersborg, Jørgen Andreas Luppino, Luigi Tommaso Anfinsen, Stian Normann Jepsen, Jane Uhd |
author_facet |
Agersborg, Jørgen Andreas Luppino, Luigi Tommaso Anfinsen, Stian Normann Jepsen, Jane Uhd |
author_sort |
Agersborg, Jørgen Andreas |
title |
Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping |
title_short |
Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping |
title_full |
Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping |
title_fullStr |
Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping |
title_full_unstemmed |
Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping |
title_sort |
toward targeted change detection with heterogeneous remote sensing images for forest mortality mapping |
publisher |
Taylor & Francis |
publishDate |
2022 |
url |
https://hdl.handle.net/10037/27111 https://doi.org/10.1080/07038992.2022.2135497 |
genre |
toundra Tundra |
genre_facet |
toundra Tundra |
op_relation |
Canadian Journal of Remote Sensing (CJRS) https://doi.org/10.1080/07038992.2022.2135497 Agersborg JAA, Luppino LT, Anfinsen SN, Jepsen JU. Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping. Canadian Journal of Remote Sensing (CJRS). 2022:1-23 FRIDAID 2063566 doi:10.1080/07038992.2022.2135497 0703-8992 1712-7971 https://hdl.handle.net/10037/27111 |
op_rights |
Attribution 4.0 International (CC BY 4.0) openAccess Copyright 2022 The Author(s) https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1080/07038992.2022.2135497 |
container_title |
Canadian Journal of Remote Sensing |
container_start_page |
1 |
op_container_end_page |
23 |
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1766218313565732864 |