Heterogeneous change detection with PRISMA and COSMO-SkyMed Second Generation imagery for natural disaster management

Change detection (CD) is among the most important tools in natural disaster monitoring. Special emphasis is on heterogeneous CD methods, which allow for a faster response. In this paper, we propose a novel heterogeneous CD method tailored at working with image domains of very different dimensionality, which allows for a greater applicational flexibility. The proposed method integrates deep image-to-image translation, spectral clustering concepts, and manifold learning, and works in a fully unsupervised manner, further enforcing a fast implementation in real-world scenarios. From an application-oriented perspective, the focus is on the recent PRISMA and COSMO-SkyMed missions of the Italian Space Agency.