| Summary: | Avalanches form a threat to people travelling in mountainous regions as well as for infrastructure and buildings. They cause around 250 fatalities annually worldwide. To limit the number of future fatalities, forecasting services are interested in knowledge on avalanche activity to verify their warning system. They rely on information about the frequency, location and extent of debris fields, as provided by avalanche experts. As avalanche terrain is mostly remote and inaccessible, it can be dangerous or even impossible to obtain necessary field information. This information is especially crucial to gain during strong winds and blowing snow when an increased avalanche danger is present. By applying Synthetic Aperture Radar (SAR), large areas can be monitored at once with both high spatial resolution and high acquisition frequency. It also has the advantage of being daylight- and weather independent. The area of interest, Nordenskiold Land on Svalbard, experiences over four months of polar darkness per year. Consequently, the most applicable technique for avalanche monitoring is SAR. Avalanche debris has an increased surface roughness compared to the surrounding unperturbed snow causing a higher backscatter signal. Therefore, the debris fields appear bright in SAR images. The main goal of this research project is to optimise avalanche detection in SAR images by exploring the option of automatic detection of debris fields. Hence, we present a method to automatically detect avalanche debris fields in SAR images. It is designed and tested on both RADARSAT-2 Ultra Fine (UF) mode and Sentinel-1A Extra Wide swath (EW) mode images. Sentinel-1A has the advantage of obtaining images twice per day over Svalbard and is made available for free. Due to the high costs to acquire RADARSAT-2 data over Svalbard, these images have a low acquisition frequency. The UF mode images are geocoded to a pixel spacing of 3m compared to 40m for the EW mode images. Both modes detect the location of debris fields, but the extent is only ...
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