TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data
Radar sounders (RSs) are nadir-looking sensors operating in high frequency (HF) or very high frequency (VHF) bands that profile subsurface targets to retrieve miscellaneous scientific information. Due to the complex electromagnetic interaction between backscattered returns, the interpretation of RS...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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ftfbkssleriris:oai:cris.fbk.eu:11582/334190 2023-11-12T04:18:47+01:00 TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data R. Ghosh F. Bovolo Ghosh, R. Bovolo, F. 2022 STAMPA http://hdl.handle.net/11582/334190 https://doi.org/10.1109/TGRS.2022.3180761 https://ieeexplore.ieee.org/document/9791273 eng eng volume:60 journal:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING http://hdl.handle.net/11582/334190 doi:10.1109/TGRS.2022.3180761 https://ieeexplore.ieee.org/document/9791273 info:eu-repo/semantics/article 2022 ftfbkssleriris https://doi.org/10.1109/TGRS.2022.3180761 2023-10-24T21:01:31Z Radar sounders (RSs) are nadir-looking sensors operating in high frequency (HF) or very high frequency (VHF) bands that profile subsurface targets to retrieve miscellaneous scientific information. Due to the complex electromagnetic interaction between backscattered returns, the interpretation of RS data is challenging. The investigations of ice-sheet subsurface structures require automatic techniques to account for both the sequential spatial distribution of subsurface targets and relevant statistical properties embedded in RS signals. Automatic techniques exist for characterizing these targets either related to probabilistic inference models or convolutional neural network (CNN) deep learning methods. Unfortunately, CNN-based methods capture local spatial context and merely model the global spatial context. In contrast to CNN, the transformer-based models are reliable architectures for capturing long-range sequence-to-sequence global spatial contextual prior. Motivated by the aforementioned fact, we propose a novel transformer-based semantic segmentation architecture named TransSounder to effectively encode the sequential structures of the RS signals. The TransSounder was constructed on a hybrid TransUNet-TransFuse architectural framework to systematically augment the modules from TransUNet and TransFuse architectures. Experimental results obtained using the Multichannel Coherent Radar Depth Sounder (MCoRDS) dataset confirms the robustness and capability of transformers to accurately characterize the different subsurface targets. Article in Journal/Newspaper Ice Sheet Fondazione Bruno Kessler: CINECA IRIS IEEE Transactions on Geoscience and Remote Sensing 60 1 13 |
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Open Polar |
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Fondazione Bruno Kessler: CINECA IRIS |
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English |
description |
Radar sounders (RSs) are nadir-looking sensors operating in high frequency (HF) or very high frequency (VHF) bands that profile subsurface targets to retrieve miscellaneous scientific information. Due to the complex electromagnetic interaction between backscattered returns, the interpretation of RS data is challenging. The investigations of ice-sheet subsurface structures require automatic techniques to account for both the sequential spatial distribution of subsurface targets and relevant statistical properties embedded in RS signals. Automatic techniques exist for characterizing these targets either related to probabilistic inference models or convolutional neural network (CNN) deep learning methods. Unfortunately, CNN-based methods capture local spatial context and merely model the global spatial context. In contrast to CNN, the transformer-based models are reliable architectures for capturing long-range sequence-to-sequence global spatial contextual prior. Motivated by the aforementioned fact, we propose a novel transformer-based semantic segmentation architecture named TransSounder to effectively encode the sequential structures of the RS signals. The TransSounder was constructed on a hybrid TransUNet-TransFuse architectural framework to systematically augment the modules from TransUNet and TransFuse architectures. Experimental results obtained using the Multichannel Coherent Radar Depth Sounder (MCoRDS) dataset confirms the robustness and capability of transformers to accurately characterize the different subsurface targets. |
author2 |
Ghosh, R. Bovolo, F. |
format |
Article in Journal/Newspaper |
author |
R. Ghosh F. Bovolo |
spellingShingle |
R. Ghosh F. Bovolo TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data |
author_facet |
R. Ghosh F. Bovolo |
author_sort |
R. Ghosh |
title |
TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data |
title_short |
TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data |
title_full |
TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data |
title_fullStr |
TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data |
title_full_unstemmed |
TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data |
title_sort |
transsounder: a hybrid transunet-transfuse architectural framework for semantic segmentation of radar sounder data |
publishDate |
2022 |
url |
http://hdl.handle.net/11582/334190 https://doi.org/10.1109/TGRS.2022.3180761 https://ieeexplore.ieee.org/document/9791273 |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_relation |
volume:60 journal:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING http://hdl.handle.net/11582/334190 doi:10.1109/TGRS.2022.3180761 https://ieeexplore.ieee.org/document/9791273 |
op_doi |
https://doi.org/10.1109/TGRS.2022.3180761 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
container_volume |
60 |
container_start_page |
1 |
op_container_end_page |
13 |
_version_ |
1782335354748010496 |