Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images

State-of-the-art deep learning technology has been successfully applied to relatively small selected areas of very high spatial resolution (0.15 and 0.25 m) optical aerial imagery acquired by a fixed-wing aircraft to automatically characterize ice-wedge polygons (IWPs) in the Arctic tundra. However,...

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Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 12; no. 7; p. 1085
Main Authors:	Zhang, Weixing, Liljedahl, Anna K., Kanevskiy, Mikhail, Epstein, Howard E., Jones, Benjamin M., Jorgenson, M. Torre, Kent, Kelcy
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 28.03.2020
Subjects:	Accuracy Aircraft Aircraft configurations Aircraft icing Alaska Annotations Arctic Arctic region Artificial neural networks Assessments Automation cameras CMOS Coastal plains data collection Datasets Deep learning Fixed wings High resolution ice-wedge polygons Image acquisition Image detection Image processing Image resolution Machine learning Mapping Mask R-CNN Model accuracy Neural networks Permafrost Polygons Remote sensing Satellite imagery Satellites Sensors Spatial discrimination Spatial resolution Spectral bands Stability analysis Studies Training Tundra UAV Unmanned aerial vehicles Wedges WorldView-2 United States > US Alaska Arctic region
ISSN:	2072-4292, 2072-4292
Online Access:	Get full text
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