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

Zhang, Weixing ; Liljedahl, Anna K. ; Kanevskiy, Mikhail ; Epstein, Howard E. ; Jones, Benjamin M. ; Jorgenson, M. Torre ; Kent, Kelcy

Remote sensing (Basel, Switzerland), 2020-04, Vol.12 (7), p.1085 [Revista revisada por pares]

Basel: MDPI AG

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Título:
Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images
Autor: Zhang, Weixing ; Liljedahl, Anna K. ; Kanevskiy, Mikhail ; Epstein, Howard E. ; Jones, Benjamin M. ; Jorgenson, M. Torre ; Kent, Kelcy
Materias: Accuracy ; Aircraft ; Aircraft configurations ; Aircraft icing ; Annotations ; Arctic ; Artificial neural networks ; Assessments ; Automation ; CMOS ; Coastal plains ; Datasets ; Deep learning ; Fixed wings ; High resolution ; ice-wedge polygons ; Image acquisition ; Image detection ; Image processing ; Image resolution ; Levels ; Machine learning ; Mapping ; Mask R-CNN ; Model accuracy ; Neural networks ; Permafrost ; Platforms ; Polar environments ; 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
Es parte de: Remote sensing (Basel, Switzerland), 2020-04, Vol.12 (7), p.1085
Descripción: 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.
Editor: Basel: MDPI AG
Idioma: Inglés

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

Zhang, Weixing ; Liljedahl, Anna K. ; Kanevskiy, Mikhail ; Epstein, Howard E. ; Jones, Benjamin M. ; Jorgenson, M. Torre ; Kent, Kelcy

Basel: MDPI AG

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