In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project "Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures," funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.
S., S., Bruzzone, L., Corsini, G., Emery, W., Gamba, P., Garzelli, A., et al. (2012). Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures. In Proc. IEEE IGARSS'12 (pp.5506-5509). IEEE [10.1109/IGARSS.2012.6352359].
Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures
GARZELLI, ANDREA;ZOPPETTI, CLAUDIA
2012-01-01
Abstract
In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project "Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures," funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.
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https://hdl.handle.net/11365/42900
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