Snow Avalanche Segmentation in SAR Images With Fully Convolutional Neural Networks

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Jakob Grahn¹  Filippo Maria Bianchi²  Hannah Vickers³  Eirik Malnes³  Markus Eckerstorfer³ 

[1] , Norway;Department of Mathematics and Statistics, UiT The Arctic University of Norway, Troms&x00F8;NORCE Norwegian Research Centre AS, Bergen, Norway;

关键词: Convolutional neural networks (CNNs);    deep learning;    saliency segmentation;    Sentinel-1 (S1);    snow avalanches;    synthetic aperture radar (SAR);   

DOI  :  10.1109/JSTARS.2020.3036914

来源: DOAJ

【 摘 要 】

Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite imagery has been developed to improve monitoring. However, the current state-of-the-art detection algorithms, based on radar signal processing techniques, are still much less accurate than human experts. To reduce this gap, we propose a deep learning architecture for detecting avalanches in Sentinel-1 radar images. We trained a neural network on 6345 manually labeled avalanches from 117 Sentinel-1 images, each one consisting of six channels that include backscatter and topographical information. Then, we tested our trained model on a new synthetic aperture radar image. Comparing to the manual labeling (the gold standard), we achieved an F1 score above 66%, whereas the state-of-the-art detection algorithm sits at an F1 score of only 38%. A visual inspection of the results generated by our deep learning model shows that only small avalanches are undetected, whereas some avalanches that were originally not labeled by the human expert are discovered.

【 授权许可】

Unknown