Snow cover area identification by using a change detection method applied to COSMO-SkyMed images

Simone Pettinato; Emanuele Santi; Simonetta Paloscia; Bruno Aiazzi; Stefano Baronti; Andrea Garzelli

doi:10.1117/1.JRS.8.084684

7 August 2014 Snow cover area identification by using a change detection method applied to COSMO-SkyMed images

Simone Pettinato, Emanuele Santi, Simonetta Paloscia, Bruno Aiazzi, Stefano Baronti, Andrea Garzelli

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 084684 (August 2014). https://doi.org/10.1117/1.JRS.8.084684

Abstract

The information theoretic snow detection algorithm, a method that employs a change detection approach derived by Shannon’s information theory based on the conditional probability of the local means between two images taken at different times, is applied to multitemporal COSMO-SkyMed® data. The ultimate purpose of the method is the identification of snow cover areas in the case of extensive surface changes between summer and winter seasons. Both Himage and Ping Pong data in Stripmap acquisition mode from the COSMO-SkyMed constellation are processed. Results are compared to the available ground snow information gathered at the meteorological station present in the area. Quantitative assessments are obtained for Himage by considering a Landsat image as ground-truth. Receiver operating characteristic curves are used to deliver numerical comparisons between ground-truth and classified image, which is then compared to the well-known log-ratio approach. The proposed information theoretical approach to change detection provides very promising results in the case of large snow covering on multitemporal single-look synthetic aperture radar images at very high spatial resolution, due to its intrinsic low sensibility to speckle noise.

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Simone Pettinato, Emanuele Santi, Simonetta Paloscia, Bruno Aiazzi, Stefano Baronti, and Andrea Garzelli "Snow cover area identification by using a change detection method applied to COSMO-SkyMed images," Journal of Applied Remote Sensing 8(1), 084684 (7 August 2014). https://doi.org/10.1117/1.JRS.8.084684

Published: 7 August 2014

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