Context-Enhanced Detector For Building Detection From Remote Sensing Images
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929414553468928 |
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| author | Huang, Ziyue Zhang, Mingming Liu, Qingjie Wang, Wei Dong, Zhe Wang, Yunhong |
| author_facet | Huang, Ziyue Zhang, Mingming Liu, Qingjie Wang, Wei Dong, Zhe Wang, Yunhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_07638 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Context-Enhanced Detector For Building Detection From Remote Sensing Images Huang, Ziyue Zhang, Mingming Liu, Qingjie Wang, Wei Dong, Zhe Wang, Yunhong Computer Vision and Pattern Recognition 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. |
| title | Context-Enhanced Detector For Building Detection From Remote Sensing Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.07638 |