Context-Enhanced Detector For Building Detection From Remote Sensing
  Images
Ziyue Huang Mingming Zhang Qingjie Liu Wei Wang Zhe Dong Yunhong Wang
The field of building detection from remote sensing images has made
significant progress, but faces challenges in achieving high-accuracy detection
due to the diversity in building appearances and the complexity of vast scenes.
To address these challenges, we propose a novel approach called
Context-Enhanced Detector (CEDet). Our approach utilizes a three-stage cascade
structure to enhance the extraction of contextual information and improve
building detection accuracy. Specifically, we introduce two modules: the
Semantic Guided Contextual Mining (SGCM) module, which aggregates multi-scale
contexts and incorporates an attention mechanism to capture long-range
interactions, and the Instance Context Mining Module (ICMM), which captures
instance-level relationship context by constructing a spatial relationship
graph and aggregating instance features. Additionally, we introduce a semantic
segmentation loss based on pseudo-masks to guide contextual information
extraction. Our method achieves state-of-the-art performance on three building
detection benchmarks, including CNBuilding-9P, CNBuilding-23P, and SpaceNet.