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

• 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
• DOI: 10.3390/rs12071085

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, any mapping of IWPs at regional to continental scales requires images acquired on different sensor platforms (particularly satellite) and a refined understanding of the performance stability of the method across sensor platforms through reliable evaluation assessments. In this study, we examined the transferability of a deep learning Mask Region-Based Convolutional Neural Network (R-CNN) model for mapping IWPs in satellite remote sensing imagery (~0.5 m) covering 272 km2 and unmanned aerial vehicle (UAV) (0.02 m) imagery covering 0.32 km2. Multi-spectral images were obtained from the WorldView-2 satellite sensor and pan-sharpened to ~0.5 m, and a 20 mp CMOS sensor camera onboard a UAV, respectively. The training dataset included 25,489 and 6022 manually delineated IWPs from satellite and fixed-wing aircraft aerial imagery near the Arctic Coastal Plain, northern Alaska. Quantitative assessments showed that individual IWPs were correctly detected at up to 72% and 70%, and delineated at up to 73% and 68% F1 score accuracy levels for satellite and UAV images, respectively. Expert-based qualitative assessments showed that IWPs were correctly detected at good (40–60%) and excellent (80–100%) accuracy levels for satellite and UAV images, respectively, and delineated at excellent (80–100%) level for both images. We found that (1) regardless of spatial resolution and spectral bands, the deep learning Mask R-CNN model effectively mapped IWPs in both remote sensing satellite and UAV images; (2) the model achieved a better accuracy in detection with finer image resolution, such as UAV imagery, yet a better accuracy in delineation with coarser image resolution, such as satellite imagery; (3) increasing the number of training data with different resolutions between the training and actual application imagery does not necessarily result in better performance of the Mask R-CNN in IWPs mapping; (4) and overall, the model underestimates the total number of IWPs particularly in terms of disjoint/incomplete IWPs.