Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods

Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kortum, Karl [verfasserIn] Singha, Suman [akademischer betreuerIn] Spreen, Gunnar [akademischer betreuerIn] Haas, Christian [akademischer betreuerIn]
Körperschaften:	Universität Bremen [Grad-verleihende Institution]
Hochschulschrift:	Dissertation ; Universität Bremen ; 2024

Format:	E-Book
Sprache:	Englisch

Erschienen:	Bremen: 2024

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	Sea Ice Machine Learning Deep Learning Synthetic Aperture Radar Physics-informed Neural Networks Altimetry
Formangabe:	Hochschulschrift

Umfang:	1 Online-Ressource (176 Seiten) ; Illustrationen

Weitere Ausgabe:	Erscheint auch als Druck-Ausgabe Kortum, Karl: Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods - Bremen, 2024

Links:	Link aufrufen Link aufrufen Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	urn:nbn:de:gbv:46-elib78032 10.26092/elib/2885

Katalog-ID:	1889737089

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author_variant	k k kk
matchkey_str	kortumkarlsinghasumanspreengunnarhaaschr:2024----:rtceierpryereafosnhtcprueaaw
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allfields	urn:nbn:de:gbv:46-elib78032 urn 10.26092/elib/2885 doi (DE-627)1889737089 (DE-599)KXP1889737089 (OCoLC)1435609701 (OAEPFHB)elib/2885 DE-627 ger DE-627 rda eng XA-DE-HB 551.34 DE-101 550 DE-101 Kortum, Karl verfasserin (orcid)0000-0002-8418-6484 aut Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods by Karl Kortum Bremen 2024 1 Online-Ressource (176 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Sea Ice Machine Learning Deep Learning Synthetic Aperture Radar Physics-informed Neural Networks Altimetry Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Singha, Suman akademischer betreuerin dgs Spreen, Gunnar akademischer betreuerin (DE-588)13614439X (DE-627)57719111X (DE-576)300857667 dgs Haas, Christian akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kortum, Karl Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods Bremen, 2024 176 Seiten (DE-627)1889737216 https://doi.org/10.26092/elib/2885 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 Resolving-System kostenfrei https://d-nb.info/1339510812/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7803 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 4593083354 x 12-10-24 21 01 0046 4528851229 ebook_2024_dissbremen Kostenloser Zugriff zza 24-05-24 22 01 0018 4593184533 SUBolrd xu 12-10-24 23 01 0830 4593232643 olr-d x 12-10-24 30 01 0104 4593279038 z 12-10-24 40 01 0007 4593315506 xsn 12-10-24 60 01 0705 4593373727 OLRD z 12-10-24 63 01 3401 4593426928 ORD x 12-10-24 70 01 0089 459348247X z 12-10-24 105 01 0841 459387209X z 12-10-24 110 01 3110 4593583160 x 12-10-24 132 01 0959 4593626773 OLR-DISS x 12-10-24 151 01 0546 459366750X OLR-ODISS z 12-10-24 161 01 0960 4593691559 ORD z 12-10-24 293 01 3293 4593821088 ORD z 12-10-24 370 01 4370 4593854563 x 12-10-24 2403 01 DE-LFER 4536367829 00 --%%-- --%%-- n --%%-- l01 11-06-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 21 01 0046 https://doi.org/10.26092/elib/2885 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 2403 01 DE-LFER https://doi.org/10.26092/elib/2885 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
spelling	urn:nbn:de:gbv:46-elib78032 urn 10.26092/elib/2885 doi (DE-627)1889737089 (DE-599)KXP1889737089 (OCoLC)1435609701 (OAEPFHB)elib/2885 DE-627 ger DE-627 rda eng XA-DE-HB 551.34 DE-101 550 DE-101 Kortum, Karl verfasserin (orcid)0000-0002-8418-6484 aut Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods by Karl Kortum Bremen 2024 1 Online-Ressource (176 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Sea Ice Machine Learning Deep Learning Synthetic Aperture Radar Physics-informed Neural Networks Altimetry Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Singha, Suman akademischer betreuerin dgs Spreen, Gunnar akademischer betreuerin (DE-588)13614439X (DE-627)57719111X (DE-576)300857667 dgs Haas, Christian akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kortum, Karl Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods Bremen, 2024 176 Seiten (DE-627)1889737216 https://doi.org/10.26092/elib/2885 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 Resolving-System kostenfrei https://d-nb.info/1339510812/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7803 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 4593083354 x 12-10-24 21 01 0046 4528851229 ebook_2024_dissbremen Kostenloser Zugriff zza 24-05-24 22 01 0018 4593184533 SUBolrd xu 12-10-24 23 01 0830 4593232643 olr-d x 12-10-24 30 01 0104 4593279038 z 12-10-24 40 01 0007 4593315506 xsn 12-10-24 60 01 0705 4593373727 OLRD z 12-10-24 63 01 3401 4593426928 ORD x 12-10-24 70 01 0089 459348247X z 12-10-24 105 01 0841 459387209X z 12-10-24 110 01 3110 4593583160 x 12-10-24 132 01 0959 4593626773 OLR-DISS x 12-10-24 151 01 0546 459366750X OLR-ODISS z 12-10-24 161 01 0960 4593691559 ORD z 12-10-24 293 01 3293 4593821088 ORD z 12-10-24 370 01 4370 4593854563 x 12-10-24 2403 01 DE-LFER 4536367829 00 --%%-- --%%-- n --%%-- l01 11-06-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 21 01 0046 https://doi.org/10.26092/elib/2885 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 2403 01 DE-LFER https://doi.org/10.26092/elib/2885 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
allfields_unstemmed	urn:nbn:de:gbv:46-elib78032 urn 10.26092/elib/2885 doi (DE-627)1889737089 (DE-599)KXP1889737089 (OCoLC)1435609701 (OAEPFHB)elib/2885 DE-627 ger DE-627 rda eng XA-DE-HB 551.34 DE-101 550 DE-101 Kortum, Karl verfasserin (orcid)0000-0002-8418-6484 aut Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods by Karl Kortum Bremen 2024 1 Online-Ressource (176 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Sea Ice Machine Learning Deep Learning Synthetic Aperture Radar Physics-informed Neural Networks Altimetry Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Singha, Suman akademischer betreuerin dgs Spreen, Gunnar akademischer betreuerin (DE-588)13614439X (DE-627)57719111X (DE-576)300857667 dgs Haas, Christian akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kortum, Karl Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods Bremen, 2024 176 Seiten (DE-627)1889737216 https://doi.org/10.26092/elib/2885 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 Resolving-System kostenfrei https://d-nb.info/1339510812/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7803 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 4593083354 x 12-10-24 21 01 0046 4528851229 ebook_2024_dissbremen Kostenloser Zugriff zza 24-05-24 22 01 0018 4593184533 SUBolrd xu 12-10-24 23 01 0830 4593232643 olr-d x 12-10-24 30 01 0104 4593279038 z 12-10-24 40 01 0007 4593315506 xsn 12-10-24 60 01 0705 4593373727 OLRD z 12-10-24 63 01 3401 4593426928 ORD x 12-10-24 70 01 0089 459348247X z 12-10-24 105 01 0841 459387209X z 12-10-24 110 01 3110 4593583160 x 12-10-24 132 01 0959 4593626773 OLR-DISS x 12-10-24 151 01 0546 459366750X OLR-ODISS z 12-10-24 161 01 0960 4593691559 ORD z 12-10-24 293 01 3293 4593821088 ORD z 12-10-24 370 01 4370 4593854563 x 12-10-24 2403 01 DE-LFER 4536367829 00 --%%-- --%%-- n --%%-- l01 11-06-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 21 01 0046 https://doi.org/10.26092/elib/2885 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 2403 01 DE-LFER https://doi.org/10.26092/elib/2885 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
allfieldsGer	urn:nbn:de:gbv:46-elib78032 urn 10.26092/elib/2885 doi (DE-627)1889737089 (DE-599)KXP1889737089 (OCoLC)1435609701 (OAEPFHB)elib/2885 DE-627 ger DE-627 rda eng XA-DE-HB 551.34 DE-101 550 DE-101 Kortum, Karl verfasserin (orcid)0000-0002-8418-6484 aut Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods by Karl Kortum Bremen 2024 1 Online-Ressource (176 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Sea Ice Machine Learning Deep Learning Synthetic Aperture Radar Physics-informed Neural Networks Altimetry Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Singha, Suman akademischer betreuerin dgs Spreen, Gunnar akademischer betreuerin (DE-588)13614439X (DE-627)57719111X (DE-576)300857667 dgs Haas, Christian akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kortum, Karl Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods Bremen, 2024 176 Seiten (DE-627)1889737216 https://doi.org/10.26092/elib/2885 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 Resolving-System kostenfrei https://d-nb.info/1339510812/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7803 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 4593083354 x 12-10-24 21 01 0046 4528851229 ebook_2024_dissbremen Kostenloser Zugriff zza 24-05-24 22 01 0018 4593184533 SUBolrd xu 12-10-24 23 01 0830 4593232643 olr-d x 12-10-24 30 01 0104 4593279038 z 12-10-24 40 01 0007 4593315506 xsn 12-10-24 60 01 0705 4593373727 OLRD z 12-10-24 63 01 3401 4593426928 ORD x 12-10-24 70 01 0089 459348247X z 12-10-24 105 01 0841 459387209X z 12-10-24 110 01 3110 4593583160 x 12-10-24 132 01 0959 4593626773 OLR-DISS x 12-10-24 151 01 0546 459366750X OLR-ODISS z 12-10-24 161 01 0960 4593691559 ORD z 12-10-24 293 01 3293 4593821088 ORD z 12-10-24 370 01 4370 4593854563 x 12-10-24 2403 01 DE-LFER 4536367829 00 --%%-- --%%-- n --%%-- l01 11-06-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 21 01 0046 https://doi.org/10.26092/elib/2885 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 2403 01 DE-LFER https://doi.org/10.26092/elib/2885 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
allfieldsSound	urn:nbn:de:gbv:46-elib78032 urn 10.26092/elib/2885 doi (DE-627)1889737089 (DE-599)KXP1889737089 (OCoLC)1435609701 (OAEPFHB)elib/2885 DE-627 ger DE-627 rda eng XA-DE-HB 551.34 DE-101 550 DE-101 Kortum, Karl verfasserin (orcid)0000-0002-8418-6484 aut Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods by Karl Kortum Bremen 2024 1 Online-Ressource (176 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB XA-DE-HB pdager DE-46 Sea Ice Machine Learning Deep Learning Synthetic Aperture Radar Physics-informed Neural Networks Altimetry Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Singha, Suman akademischer betreuerin dgs Spreen, Gunnar akademischer betreuerin (DE-588)13614439X (DE-627)57719111X (DE-576)300857667 dgs Haas, Christian akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kortum, Karl Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods Bremen, 2024 176 Seiten (DE-627)1889737216 https://doi.org/10.26092/elib/2885 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 Resolving-System kostenfrei https://d-nb.info/1339510812/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7803 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 4593083354 x 12-10-24 21 01 0046 4528851229 ebook_2024_dissbremen Kostenloser Zugriff zza 24-05-24 22 01 0018 4593184533 SUBolrd xu 12-10-24 23 01 0830 4593232643 olr-d x 12-10-24 30 01 0104 4593279038 z 12-10-24 40 01 0007 4593315506 xsn 12-10-24 60 01 0705 4593373727 OLRD z 12-10-24 63 01 3401 4593426928 ORD x 12-10-24 70 01 0089 459348247X z 12-10-24 105 01 0841 459387209X z 12-10-24 110 01 3110 4593583160 x 12-10-24 132 01 0959 4593626773 OLR-DISS x 12-10-24 151 01 0546 459366750X OLR-ODISS z 12-10-24 161 01 0960 4593691559 ORD z 12-10-24 293 01 3293 4593821088 ORD z 12-10-24 370 01 4370 4593854563 x 12-10-24 2403 01 DE-LFER 4536367829 00 --%%-- --%%-- n --%%-- l01 11-06-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 21 01 0046 https://doi.org/10.26092/elib/2885 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 2403 01 DE-LFER https://doi.org/10.26092/elib/2885 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16
language	English
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topic_facet	Sea Ice Machine Learning Deep Learning Synthetic Aperture Radar Physics-informed Neural Networks Altimetry
hochschulschrift_txt_mv	Dissertation Universität Bremen 2024
sw_local_iln_str_mv	21:Universität Bremen DE-46:Universität Bremen 21:Fachbereich 01: Physik/Elektrotechnik (FB 01) DE-46:Fachbereich 01: Physik/Elektrotechnik (FB 01)
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authorswithroles_txt_mv	Kortum, Karl @@aut@@ Singha, Suman @@dgs@@ Spreen, Gunnar @@dgs@@ Haas, Christian @@dgs@@
publishDateDaySort_date	2024-01-01T00:00:00Z
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topic_title	551.34 DE-101 550 DE-101 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods by Karl Kortum Sea Ice Machine Learning Deep Learning Synthetic Aperture Radar Physics-informed Neural Networks Altimetry
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title_full	Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods by Karl Kortum
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title_sort	arctic sea ice property retrieval from synthetic aperture radar with deep learning methods
title_auth	Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods
abstract	Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change.
abstractGer	Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change.
abstract_unstemmed	Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. These techniques enable the extrapolation of sparse reference data to a large space of sea ice conditions and enable high resolution mapping of the Earth's region most affected by human-made climate change.
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title_short	Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods
url	https://doi.org/10.26092/elib/2885 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib78032 https://d-nb.info/1339510812/34 https://media.suub.uni-bremen.de/handle/elib/7803
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up_date	2024-10-30T06:38:44.748Z
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A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large timeseries of SAR data and near-coincident ground measurements were acquired for the first time. This thesis uses the unique opportunity provided by these data to analyse the behaviour of deep learning models. Seven months of data from the campaign is classified and analysed, using newly developed techniques to enable robust predictions across the timeseries. Core features are identified to facilitate robust and high-resolution classification. The final challenge of ground truth sparsity is then overcome using innovative network configurations that enable the training of 99.99%$ of the model parameters without any ground truth data. The techniques open up sea ice property retrieval to big data technologies, relying only on the abundantly available SAR data. 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