AUTOMATED RECOGNITION OF PERMAFROST DISTURBANCES USING HIGH-SPATIAL RESOLUTION SATELLITE IMAGERY AND DEEP LEARNING MODELS

• Published in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Main Authors: M. R. Udawalpola, C. Witharana, A. Hasan, A. Liljedahl, M. Ward Jones, B. Jones
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: Technology; T; Engineering (General). Civil engineering (General); TA1-2040; Applied optics. Photonics; TA1501-1820; Arctic; Ellesmere Island; Canada; Pyramid; Banks Island; Climate change; permafrost
• Online Access: https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-203-2022; https://doaj.org/article/62b06d81676749288a76f1e67b55300a

The accelerated warming conditions of the high Arctic have intensified the extensive thawing of permafrost. Retrogressive thaw slumps (RTSs) are considered as the most active landforms in the Arctic permafrost. An increase in RTSs has been observed in the Arctic in recent decades. Continuous monitoring of RTSs is important to understand climate change-driven disturbances in the region. Manual detection of these landforms is extremely difficult as they occur over exceptionally large areas. Only very few studies have explored the utility of very high spatial resolution (VHSR) commercial satellite imagery in the automated mapping of RTSs. We have developed deep learning (DL) convolution neural net (CNN) based workflow to automatically detect RTSs from VHRS satellite imagery. This study systematically compared the performance of different DLCNN model architectures and varying backbones. Our candidate CNN models include: DeepLabV3+, UNet, UNet++, Multi-scale Attention Net (MA-Net), and Pyramid Attention Network (PAN) with ResNet50, ResNet101 and ResNet152 backbones. The RTS modeling experiment was conducted on Banks Island and Ellesmere Island in Canada. The UNet++ model demonstrated the highest accuracy (F1 score of 87%) with the ResNet50 backbone at the expense of training and inferencing time. PAN, DeepLabV3, MaNet, and UNet, models reported mediocre F1 scores of 72%, 75%, 80%, and 81% respectively. Our findings unravel the performances of different DLCNNs in imagery-enabled RTS mapping and provide useful insights on operationalizing the mapping application across the Arctic.