In this study, multi-polarization Synthetic Aperture Radar (SAR) features extracted from Sentinel-1 C-band SAR measurements are used to identify wildfires and to classify burn severity. SAR features include co-and cross-polarized normalized radar cross sections and the total backscattered power, namely the SPAN. The test case refers to the wildfire that affected about 10 km2 in Tuscany region (Central Italy) during September 2018. Experiments, undertaken on actual SAR data, collected before and after the considered wildfire, demonstrate the soundness of the proposed approach and the different sensitivity of the multi-polarization backscattering features to burn severity.
Multi-polarization methods to detect and classify burned areas using Sentinel-1 SAR data / Ferrentino, E.; Nunziata, F.; Buono, A.; Sarti, M.; Migliaccio, M.. - (2021), pp. 217-221. (Intervento presentato al convegno 6th International Forum on Research and Technology for Society and Industry, RTSI 2021 tenutosi a Virtual) [10.1109/RTSI50628.2021.9597245].
Multi-polarization methods to detect and classify burned areas using Sentinel-1 SAR data
Nunziata F.;Migliaccio M.
2021
Abstract
In this study, multi-polarization Synthetic Aperture Radar (SAR) features extracted from Sentinel-1 C-band SAR measurements are used to identify wildfires and to classify burn severity. SAR features include co-and cross-polarized normalized radar cross sections and the total backscattered power, namely the SPAN. The test case refers to the wildfire that affected about 10 km2 in Tuscany region (Central Italy) during September 2018. Experiments, undertaken on actual SAR data, collected before and after the considered wildfire, demonstrate the soundness of the proposed approach and the different sensitivity of the multi-polarization backscattering features to burn severity.File | Dimensione | Formato | |
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