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1 Department of Electrical Engineering 2 GIPSA-SIGMAPHY - SIGMAPHY GIPSA-DIS - Département Images et Signal 3 LTHE - Laboratoire d-étude des transferts en hydrologie et environnement 4 University of Iceland Reykjavik

Abstract : This paper introduces an alternative strategy for wet snow detection using multitemporal SAR data. The proposed change detection method is primarily based on the comparison between two X band SAR images acquired during the accumulation winter and the melting spring seasons, in the French Alps. The new decision criterion relies on the local intensity statistics of the SAR images by considering the backscattering ratio as a stochastic process: the probability that -the intensity ratio fits into the predetermined range of values- is larger than a defined confidence level. Both the conducted snow backscattering simulations and the state of the art measurements indicate more complex relation between the backscattering properties of the two snow types, with respect to the conventional assumption of the augmented electromagnetic absorption associated to the wet snow. Therefore, rather than adopting the standard hypothesis, we analyse the wet-dry snow backscattering ratio as a function of the local incidence angle LIA. After employing the multi-layer snow backscattering simulator, calibrated with scatterometer measurements in C band, we modify, to some extent, the range of ratio values indicating the presence of the wet snow, by including positive ratio values for lower LIA. By simultaneously accounting for the speckle noise, the proposed stochastic approach derives the refined wet snow probability map. The performance analyses are carried out both through the comparison with the ground air temperature map and by comparing two co-polarized channels processed separately.

Keywords : change detection wet snow stochastic approach backscattering simulation

Autor: Nikola Besic - Gabriel Vasile - Jean-Pierre Dedieu - Jocelyn Chanussot - Srdjan Stankovic -



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