The diversity of building architecture styles of global cities situated on
various landforms, the degraded optical imagery affected by clouds and shadows,
and the significant inter-class imbalance of roof types pose challenges for
designing a robust and accurate building roof instance segmentor. To address
these issues, we propose an effective framework to fulfill semantic
interpretation of individual buildings with high-resolution optical satellite
imagery. Specifically, the leveraged domain adapted pretraining strategy and
composite dual-backbone greatly facilitates the discriminative feature
learning. Moreover, new data augmentation pipeline, stochastic weight averaging
(SWA) training and instance segmentation based model ensemble in testing are
utilized to acquire additional performance boost. Experiment results show that
our approach ranks in the first place of the 2023 IEEE GRSS Data Fusion Contest
(DFC) Track 1 test phase ($mAP_{50}$:50.6\%). Note-worthily, we have also
explored the potential of multimodal data fusion with both optical satellite
imagery and SAR data.

我们提出了一个有效的框架来实现对高分辨率光学卫星图像中个别建筑物的语义解释，通过采用领域自适应预训练策略和复合双支干，以及新的数据增强流程、随机权重平均训练和基于实例分割的模型集成，实现了额外的性能提升。值得注意的是，我们还探讨了光学卫星图像和 SAR 数据的多模态数据融合的潜力。