Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets

Tang，Xueyuan1,2; Dong，Sheng1,3,4; Luo，Kun1,5; Guo，Jingxue1; Li，Lin1; Sun，Bo1

2022

10.3390/rs14020399

Remote Sensing   影响因子和分区

2072-4292

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.

Denoising Feature extraction Ice sheet Ice-penetrating radar Machine learning Subglacial conditions

SCI ; EI

英语

其他

National Natural Science Foundation of China[41876230];National Natural Science Foundation of China[41941006];

WOS:000758887800001

20220411492407

Data handling ; Extraction ; Feature extraction ; Glaciers ; Machine learning

Data Processing and Image Processing:723.2 ; Chemical Operations:802.3

2-s2.0-85122993296

Scopus

1.Key Laboratory of Polar Science of Ministry of Natural Resources (MNR),Polar Research Institute of China,Shanghai,200136,China
2.School of Oceanography,Shanghai Jiao Tong University,Shanghai,200030,China
3.School of Earth and Space Sciences,University of Science and Technology of China,Hefei,230026,China
4.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,518055,China
5.College of Geo-Exploration Science and Technology,Jilin University,Changchun,130026,China

Tang，Xueyuan,Dong，Sheng,Luo，Kun,et al. Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets[J]. Remote Sensing,2022,14(2).

Tang，Xueyuan,Dong，Sheng,Luo，Kun,Guo，Jingxue,Li，Lin,&Sun，Bo.(2022).Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets.Remote Sensing,14(2).

Tang，Xueyuan,et al."Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets".Remote Sensing 14.2(2022).