Attention-Gate-Based Encoder-Decoder Network for Automatical Building Extraction

Rapidly developing remote sensing technology provides massive data for urban planning, mapping, and disaster management. As a carrier of human productive activities, buildings are essential to both urban dynamic monitoring and suburban construction inspection. Fully-convolutional-network-based metho...

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Veröffentlicht in:IEEE journal of selected topics in applied earth observations and remote sensing Jg. 14; S. 2611 - 2620
Hauptverfasser: Deng, Wenjing, Shi, Qian, Li, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Attention gate (AG)
building extraction
Buildings
Coders
Complexity
Construction
Construction inspection
Convolution
Datasets
Decoding
deep learning
Disaster management
Emergency preparedness
Feature extraction
fully convolutional networks (FCNs)
Image resolution
Imagery
Inspection
Logic gates
Receptive field
Remote sensing
semantic segmentation
Semantics
Task analysis
Unmanned aerial vehicles
Urban planning
ISSN:1939-1404, 2151-1535
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Zusammenfassung:Rapidly developing remote sensing technology provides massive data for urban planning, mapping, and disaster management. As a carrier of human productive activities, buildings are essential to both urban dynamic monitoring and suburban construction inspection. Fully-convolutional-network-based methods have provided a paradigm for automatically extracting buildings from high-resolution imagery. However, high intraclass variance and complexity are two problems in building extraction. It is hard to identify different scales of buildings by using a single receptive field. For this purpose, in this article, we use the stable encoder- decoder architecture, combined with a grid-based attention gate and atrous spatial pyramid pooling module, to capture and restore features progressively and effectively. A modified ResNet50 encoder is also applied to extract features. The proposed method could learn gated features and distinguish buildings from complex surroundings such as trees. We evaluate our model on two building datasets, WHU aerial building dataset and our DB UAV rural building dataset. Experiments show that our model outperforms other five most recent models. The results also exhibit great potential for extracting buildings with different scales and validate the effectiveness of deep learning in practical scenarios.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2021.3058097