TRAINING DATASET FOR THE MACHINE LEARNING APPROACH IN GLACIER MONITORING APPLYING SAR DATA

被引：0

作者：

Piwowar, Lukasz ^{[1
]}

Lucka, Magdalena ^{[1
]}

Witkowski, Wojciech ^{[1
]}

机构：

[1] AGH Univ Sci & Technol, Fac Geodata Sci Geodesy & Environm Engn, Aleja Mickiewicza 30, PL-30059 Krakow, Poland

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

machine learning; synthetic training set; glacier motion; offset-tracking; Jakobshavn glacier; MOTION;

D O I：

10.1109/IGARSS52108.2023.10281675

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The study analysed the possibility of utilizing machine learning to determine glacier displacements. The obtained results were compared with the offset-tracking method available in SNAP software. The Jakobshavn glacier in Greenland served as the test site. Analyses were carried out using Sentinel-1 data during the period of August 1 to August 7, 2021. To generate a dataset for the selected part of the glacier, a synthetic training dataset comprising 4,500 samples was created. It was constructed by applying rotation in the range of +/- 30 degrees and resizing within the range of +/- 10-20 pixels to the original patch. The final neural network (NN) consisted of 7 layers. The maximum displacement value is 250 m, corresponding to a velocity of 41 m/day. Notably, these maximum values are consistent with the results from offset-tracking. Nevertheless, the results in the slowly moving areas are not reliable because of the coarse resolution of the NN output.

引用

页码：191 / 194

页数：4

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