Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization
Remote sensing is the discipline that studies acquisition, preparation and analysis of spectral, spatial and temporal properties of objects without direct touch or contact. It is a field of great importance to understanding the climate system and its changes, as well as for conducting operations in...
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ftunivtroemsoe:oai:munin.uit.no:10037/21912 2023-05-15T14:56:23+02:00 Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization Nilsen, Torjus 2021-06-01 https://hdl.handle.net/10037/21912 eng eng UiT Norges arktiske universitet UiT The Arctic University of Norway https://hdl.handle.net/10037/21912 Copyright 2021 The Author(s) Machine Learning VDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411 Semi-Supervised Graph based Multimodal Remote Sensing GLCM MBO Label propagation MixMatch FYS-3941 Mastergradsoppgave Master thesis 2021 ftunivtroemsoe 2021-08-04T22:53:28Z Remote sensing is the discipline that studies acquisition, preparation and analysis of spectral, spatial and temporal properties of objects without direct touch or contact. It is a field of great importance to understanding the climate system and its changes, as well as for conducting operations in the Arctic. A current challenge however is that most sensory equipment can only capture one or fewer of the characteristics needed to accurately describe ground objects through their temporal, spatial, spectral and radiometric resolution characteristics. This in turn motivates the fusing of complimentary modalities for potentially improved accuracy and stability in analysis but it also leads to problems when trying to merge heterogeneous data with different statistical, geometric and physical qualities. Another concern in the remote sensing of arctic regions is the scarcity of high quality labeled data but simultaneous abundance of unlabeled data as the gathering of labeled data can be both costly and time consuming. It could therefore be of great value to explore routes that can automate this process in ways that target both the situation regarding available data and the difficulties from fusing of heterogeneous multimodal data. To this end Semi-Supervised methods were considered for their ability to leverage smaller amounts of carefully labeled data in combination with more widely available unlabeled data in achieving greater classification performance. Strengths and limitations of three algorithms for real life applications are assessed through experiments on datasets from arctic and urban areas. The first two algorithms, Deep Semi-Supervised Label Propagation (LP) and MixMatch Holistic SSL (MixMatch), consider simultaneous processing of multimodal remote sensing data with additional extracted Gray Level Co-occurrence Matrix texture features for image classification. LP trains in alternating steps of supervised learning on potentially pseudolabeled data and steps of deciding new labels through node propagation while MixMatch mixes loss terms from several leading algorithms to gain their respective benefits. Another method, Graph Fusion Merriman Bence Osher (GMBO), explores processing of modalities in parallel by constructing a fused graph from complimentary input modalities and Ginzburg-Landau minimization on an approximated Graph Laplacian. Results imply that inclusion of extracted GLCM features could be beneficial for classification of multimodal remote sensing data, and that GMBO has merits for operational use in the Arctic given that certain data prerequisites are met. Master Thesis Arctic Sea ice University of Tromsø: Munin Open Research Archive Arctic |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
Machine Learning VDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411 Semi-Supervised Graph based Multimodal Remote Sensing GLCM MBO Label propagation MixMatch FYS-3941 |
spellingShingle |
Machine Learning VDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411 Semi-Supervised Graph based Multimodal Remote Sensing GLCM MBO Label propagation MixMatch FYS-3941 Nilsen, Torjus Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization |
topic_facet |
Machine Learning VDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411 Semi-Supervised Graph based Multimodal Remote Sensing GLCM MBO Label propagation MixMatch FYS-3941 |
description |
Remote sensing is the discipline that studies acquisition, preparation and analysis of spectral, spatial and temporal properties of objects without direct touch or contact. It is a field of great importance to understanding the climate system and its changes, as well as for conducting operations in the Arctic. A current challenge however is that most sensory equipment can only capture one or fewer of the characteristics needed to accurately describe ground objects through their temporal, spatial, spectral and radiometric resolution characteristics. This in turn motivates the fusing of complimentary modalities for potentially improved accuracy and stability in analysis but it also leads to problems when trying to merge heterogeneous data with different statistical, geometric and physical qualities. Another concern in the remote sensing of arctic regions is the scarcity of high quality labeled data but simultaneous abundance of unlabeled data as the gathering of labeled data can be both costly and time consuming. It could therefore be of great value to explore routes that can automate this process in ways that target both the situation regarding available data and the difficulties from fusing of heterogeneous multimodal data. To this end Semi-Supervised methods were considered for their ability to leverage smaller amounts of carefully labeled data in combination with more widely available unlabeled data in achieving greater classification performance. Strengths and limitations of three algorithms for real life applications are assessed through experiments on datasets from arctic and urban areas. The first two algorithms, Deep Semi-Supervised Label Propagation (LP) and MixMatch Holistic SSL (MixMatch), consider simultaneous processing of multimodal remote sensing data with additional extracted Gray Level Co-occurrence Matrix texture features for image classification. LP trains in alternating steps of supervised learning on potentially pseudolabeled data and steps of deciding new labels through node propagation while MixMatch mixes loss terms from several leading algorithms to gain their respective benefits. Another method, Graph Fusion Merriman Bence Osher (GMBO), explores processing of modalities in parallel by constructing a fused graph from complimentary input modalities and Ginzburg-Landau minimization on an approximated Graph Laplacian. Results imply that inclusion of extracted GLCM features could be beneficial for classification of multimodal remote sensing data, and that GMBO has merits for operational use in the Arctic given that certain data prerequisites are met. |
format |
Master Thesis |
author |
Nilsen, Torjus |
author_facet |
Nilsen, Torjus |
author_sort |
Nilsen, Torjus |
title |
Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization |
title_short |
Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization |
title_full |
Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization |
title_fullStr |
Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization |
title_full_unstemmed |
Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization |
title_sort |
extracting information from multimodal remote sensing data for sea ice characterization |
publisher |
UiT Norges arktiske universitet |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/21912 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
https://hdl.handle.net/10037/21912 |
op_rights |
Copyright 2021 The Author(s) |
_version_ |
1766328434842140672 |