Automated change detection is certainly one of the most discussed applications of remote sensing. However, existing approaches rely on finely co-registered images, as otherwise differences in view point or illumination could lead to erroneous change detections. This is particularly true for very-high-resolution sensors, for which even minor differences might affect several resolution cells. In this paper, we make use of deep learning-based single image height prediction to transform highly non-similar synthetic aperture radar (SAR) images acquired from different viewing angles into homogeneous height maps. The change detection is then carried out in these height maps, thus mitigating any former geometric or radiometric differences of the imagery. An experiment with Capella data observing the city of Mariupol during the Russian war against Ukraine illustrates both the potential and possible risks of the approach.    «

Automated change detection is certainly one of the most discussed applications of remote sensing. However, existing approaches rely on finely co-registered images, as otherwise differences in view point or illumination could lead to erroneous change detections. This is particularly true for very-high-resolution sensors, for which even minor differences might affect several resolution cells. In this paper, we make use of deep learning-based single image height prediction to transform highly non-s...    »