Context-Enhanced Detector For Building Detection From Remote Sensing Images

• URL: http://arxiv.org/abs/2310.07638v2
• Date: Tue, 9 Jul 2024 07:03:02 GMT
• Title: Context-Enhanced Detector For Building Detection From Remote Sensing Images
• Authors: Ziyue Huang, Mingming Zhang, Qingjie Liu, Wei Wang, Zhe Dong, Yunhong Wang,
• Abstract summary: 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. Our method achieves state-of-the-art performance on three building detection benchmarks, including CNBuilding-9P, CNBuilding-23P, and SpaceNet.
• Score: 41.3238458718635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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