COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE
The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resol...
Published in: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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ftdoajarticles:oai:doaj.org/article:3fa2f13d7367410d98442df572ad0d0e 2023-05-15T14:51:39+02:00 COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE X. Shen X. Liu Y. Yao T. Feng 2021-06-01T00:00:00Z https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-497-2021 https://doaj.org/article/3fa2f13d7367410d98442df572ad0d0e EN eng Copernicus Publications https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/497/2021/isprs-archives-XLIII-B3-2021-497-2021.pdf https://doaj.org/toc/1682-1750 https://doaj.org/toc/2194-9034 doi:10.5194/isprs-archives-XLIII-B3-2021-497-2021 1682-1750 2194-9034 https://doaj.org/article/3fa2f13d7367410d98442df572ad0d0e The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2021, Pp 497-502 (2021) Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 article 2021 ftdoajarticles https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-497-2021 2022-12-31T06:51:12Z The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resolution. However, the spatial resolution of passive microwave images is relatively low (6.25 km – 25 km) for the observation of detailed sea ice characteristics and small-scale sea ice geographical phenomena. Therefore, in this paper, considering the suitability of different alignment and fusion strategies to the characteristics of passive microwave images of sea ice, two multi-images deep learning super-resolution (SR) algorithms, Recurrent Back-Projection Network (RBPN) and network of Temporal Group Attention (TGA), are selected to test the effects of SR technique for passive microwave images of sea ice. Both qualitative and quantitative comparisons are provided for the SR results oriented from two algorithms. Overall, the SR performance of TGA algorithm outperforms RBPN algorithm for the passive microwave images of sea ice. Article in Journal/Newspaper Arctic Climate change Sea ice Directory of Open Access Journals: DOAJ Articles Arctic The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 497 502 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
spellingShingle |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 X. Shen X. Liu Y. Yao T. Feng COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE |
topic_facet |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
description |
The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resolution. However, the spatial resolution of passive microwave images is relatively low (6.25 km – 25 km) for the observation of detailed sea ice characteristics and small-scale sea ice geographical phenomena. Therefore, in this paper, considering the suitability of different alignment and fusion strategies to the characteristics of passive microwave images of sea ice, two multi-images deep learning super-resolution (SR) algorithms, Recurrent Back-Projection Network (RBPN) and network of Temporal Group Attention (TGA), are selected to test the effects of SR technique for passive microwave images of sea ice. Both qualitative and quantitative comparisons are provided for the SR results oriented from two algorithms. Overall, the SR performance of TGA algorithm outperforms RBPN algorithm for the passive microwave images of sea ice. |
format |
Article in Journal/Newspaper |
author |
X. Shen X. Liu Y. Yao T. Feng |
author_facet |
X. Shen X. Liu Y. Yao T. Feng |
author_sort |
X. Shen |
title |
COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE |
title_short |
COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE |
title_full |
COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE |
title_fullStr |
COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE |
title_full_unstemmed |
COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE |
title_sort |
comparison of multi-images deep learning super resolution for passive microwave images of arctic sea ice |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-497-2021 https://doaj.org/article/3fa2f13d7367410d98442df572ad0d0e |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Sea ice |
genre_facet |
Arctic Climate change Sea ice |
op_source |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2021, Pp 497-502 (2021) |
op_relation |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/497/2021/isprs-archives-XLIII-B3-2021-497-2021.pdf https://doaj.org/toc/1682-1750 https://doaj.org/toc/2194-9034 doi:10.5194/isprs-archives-XLIII-B3-2021-497-2021 1682-1750 2194-9034 https://doaj.org/article/3fa2f13d7367410d98442df572ad0d0e |
op_doi |
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-497-2021 |
container_title |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume |
XLIII-B3-2021 |
container_start_page |
497 |
op_container_end_page |
502 |
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1766322786544910336 |