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.

该研究提出了一种基于多模态卫星数据的新型多时相城市制图方法，其中考虑到了部分缺失的光学模态，所提出的模型采用了两个网络来分别提取每个模态的特征，并利用重建网络根据 SAR 数据近似光学特征并保持其数据的有效性。结果表明该方法在使用 SAR 数据以补充缺失光学数据方面的实用性较强，可有效开发和利用多模态数据。