Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone
This work provides an incremental improvement for remote sensing applications by enhancing building roof instance segmentation accuracy.
The paper tackles the challenge of accurately segmenting individual building roofs from high-resolution satellite imagery, addressing issues like architectural diversity and image degradation, and achieves first place in the 2023 IEEE GRSS Data Fusion Contest with a mAP50 of 50.6%.
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.