This paper addresses the problem of exploiting very high-resolution multifrequency SAR data collected by the COSMO-SkyMed and RADARSAT-2 missions to support risk monitoring and assessment in urban and suburban areas. The proposed approach aims at taking benefit from the synergy between the two SAR data sources to optimize the accuracy of thematic products of interest to risk monitoring. In particular, an unsupervised change detection approach is discussed, in which feature-level fusion is applied to a satellite image time series including both COSMO-SkyMed and RADARSAT-2 acquisitions to improve the detection results as compared to those generated through single-frequency processing.