Query：
学者姓名：余大文
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
Abstract ：
Bird's-eye-view (BEV) building mapping from remote sensing images is a studying hotspot with broad applications. In recent years, deep learning (DL) has significantly advanced the development of automatic building extraction methods. However, most existing research focuses on segmenting buildings from a single perspective, such as orthophotos, overlooking the rich information of multi-view images. In surveying and mapping, individual building instances need to be separated even when they are adjacent or touching. Since orthophotos cannot capture building walls due to self-occlusion, distinguishing between closely connected buildings in densely built areas becomes challenging. To tackle this issue, we propose a multi-view collaborative pipeline for instance-level building segmentation. This pipeline utilizes a grouping optimization algorithm to merge segmentation results from multiple views, which are predicted by general instance segmentation networks and projected onto the BEV, to produce the final building instance polygons. Both qualitative and quantitative results show that the proposed multi-view collaborative pipeline significantly outperforms the popular orthophoto-based pipeline on the InstanceBuilding dataset.
Keyword ：
Annotations Annotations Building extraction Building extraction Buildings Buildings Clustering algorithms Clustering algorithms Collaboration Collaboration convolutional neural networks (CNNs) convolutional neural networks (CNNs) Data mining Data mining grouping optimization algorithm grouping optimization algorithm Image segmentation Image segmentation instance segmentation instance segmentation Instance segmentation Instance segmentation multi-view aerial images multi-view aerial images Optimization Optimization Pipelines Pipelines Training Training
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yu, Dawen , Cheng, Hao . Building Extraction From Multi-View RGB-H Images With General Instance Segmentation Networks and a Grouping Optimization Algorithm [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2025 , 22 . |
| MLA | Yu, Dawen 等. "Building Extraction From Multi-View RGB-H Images With General Instance Segmentation Networks and a Grouping Optimization Algorithm" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 22 (2025) . |
| APA | Yu, Dawen , Cheng, Hao . Building Extraction From Multi-View RGB-H Images With General Instance Segmentation Networks and a Grouping Optimization Algorithm . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2025 , 22 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Instance segmentation performance in remote sensing images (RSIs) is significantly affected by two issues: how to extract accurate boundaries of objects from remote imaging through the dynamic atmosphere, and how to integrate the mutual information of related object instances scattered over a vast spatial region. In this study, we propose a novel shape guided transformer network (SGTN) to accurately extract objects at the instance level. Inspired by the global contextual modeling capacity of the self-attention mechanism, we propose an effective transformer encoder termed LSwin, which incorporates vertical and horizontal 1-D global self-attention mechanisms to obtain better global-perception capacity for RSIs than the popular local-shifted-window based swin transformer. To achieve accurate instance mask segmentation, we introduce a shape guidance module (SGM) to emphasize the object boundary and shape information. The combination of SGM, which emphasizes the local detail information, and LSwin, which focuses on the global context relationships, achieve excellent RSI instance segmentation. Their effectiveness was validated through comprehensive ablation experiments. Especially, LSwin is proven better than the popular ResNet and swin transformer encoders at the same level of efficiency. Compared to other instance segmentation methods, our SGTN achieves the highest average precision scores on two single-class public datasets (WHU dataset and BITCC dataset) and a multiclass public dataset (NWPU VHR-10 dataset).
Keyword ：
Accuracy Accuracy Computational modeling Computational modeling Convolution Convolution Correlation Correlation Feature extraction Feature extraction Instance segmentation Instance segmentation long-range correlation long-range correlation Recurrent neural networks Recurrent neural networks Remote sensing Remote sensing remote sensing image (RSI) remote sensing image (RSI) Shape Shape shape enhancement shape enhancement transformer network transformer network Transformers Transformers
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yu, Dawen , Ji, Shunping . A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 : 8325-8339 . |
| MLA | Yu, Dawen 等. "A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18 (2025) : 8325-8339 . |
| APA | Yu, Dawen , Ji, Shunping . A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 , 8325-8339 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
