Backscatter Characteristics of Snow Avalanches for Mapping With Local Resolution Weighting

• Main Authors: Cedric Tompkin, Silvan Leinss
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Radar remote sensing; Radar imaging; Radar detection; Synthetic aperture radar; Electromagnetic reflection; Supervised learning
• Online Access: https://ieeexplore.ieee.org/document/9409959/

Snow avalanches cause a sudden change of snow properties making them detectable with synthetic aperture radar (SAR). However, steep alpine terrain combined with the slant view geometry of SAR sensors complicates detection: the avalanche brightness depends on the incidence angle and the observed area is limited by radar layover and shadow. Likewise, the spatial resolution varies strongly with the local incidence angle relative to the terrain. To increase the avalanche brightness and to improve the imaging coverage and resolution we apply local resolution weighting (LRW) on Sentinel-1 (S1) backscatter images from ascending and descending orbits. LRW merges acquisitions by averaging them, weighted with the local ground-range resolution. To analyze the relative avalanche brightness with respect to the local incidence angle and polarization, and to quantify the benefit of LRW, we created a dataset of 914 manually drawn avalanche outlines based on S1 imagery of an extreme avalanche event on January 4, 2018 in the Swiss Alps. We show that avalanches appear brightest at slopes facing away from the radar at local incidence angles of <inline-formula><tex-math notation="LaTeX">$55\pm 20^\circ$</tex-math></inline-formula>; such slopes are weighted considerably stronger through LRW. With a processing pipeline for avalanche segmentation using a fixed threshold on the backscatter difference we obtain a higher F1 score (0.75) with LRW compared to an unweighted orbit average (F1 = 0.68) or single orbit acquisitions (F1 = 0.5). For automatic segmentation, we used the manually drawn dataset for training and testing of a deep neural U-Net and achieved a F1 score of 0.81 on LRW backscatter differences.