Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets
The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambien...
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ftmdpi:oai:mdpi.com:/2072-4292/14/2/399/ 2023-08-20T04:07:14+02:00 Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets Xueyuan Tang Sheng Dong Kun Luo Jingxue Guo Lin Li Bo Sun agris 2022-01-16 application/pdf https://doi.org/10.3390/rs14020399 EN eng Multidisciplinary Digital Publishing Institute Engineering Remote Sensing https://dx.doi.org/10.3390/rs14020399 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 2; Pages: 399 ice-penetrating radar denoising feature extraction subglacial conditions ice sheet machine learning Text 2022 ftmdpi https://doi.org/10.3390/rs14020399 2023-08-01T03:51:06Z The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction. Text Ice Sheet MDPI Open Access Publishing Remote Sensing 14 2 399 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
ice-penetrating radar denoising feature extraction subglacial conditions ice sheet machine learning |
spellingShingle |
ice-penetrating radar denoising feature extraction subglacial conditions ice sheet machine learning Xueyuan Tang Sheng Dong Kun Luo Jingxue Guo Lin Li Bo Sun Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets |
topic_facet |
ice-penetrating radar denoising feature extraction subglacial conditions ice sheet machine learning |
description |
The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction. |
format |
Text |
author |
Xueyuan Tang Sheng Dong Kun Luo Jingxue Guo Lin Li Bo Sun |
author_facet |
Xueyuan Tang Sheng Dong Kun Luo Jingxue Guo Lin Li Bo Sun |
author_sort |
Xueyuan Tang |
title |
Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets |
title_short |
Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets |
title_full |
Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets |
title_fullStr |
Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets |
title_full_unstemmed |
Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets |
title_sort |
noise removal and feature extraction in airborne radar sounding data of ice sheets |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14020399 |
op_coverage |
agris |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_source |
Remote Sensing; Volume 14; Issue 2; Pages: 399 |
op_relation |
Engineering Remote Sensing https://dx.doi.org/10.3390/rs14020399 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14020399 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
2 |
container_start_page |
399 |
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1774718721415184384 |