Multi-Modal Deep Learning for Multi-Temporal Urban Mapping With a Partly
  Missing Optical Modality
Sebastian Hafner Yifang Ban
This paper proposes a novel multi-temporal urban mapping approach using
multi-modal satellite data from the Sentinel-1 Synthetic Aperture Radar (SAR)
and Sentinel-2 MultiSpectral Instrument (MSI) missions. In particular, it
focuses on the problem of a partly missing optical modality due to clouds. The
proposed model utilizes two networks to extract features from each modality
separately. In addition, a reconstruction network is utilized to approximate
the optical features based on the SAR data in case of a missing optical
modality. Our experiments on a multi-temporal urban mapping dataset with
Sentinel-1 SAR and Sentinel-2 MSI data demonstrate that the proposed method
outperforms a multi-modal approach that uses zero values as a replacement for
missing optical data, as well as a uni-modal SAR-based approach. Therefore, the
proposed method is effective in exploiting multi-modal data, if available, but
it also retains its effectiveness in case the optical modality is missing.