Publication: PCL-PTD Net: Parallel Cross-Learning-Based Pixel Transferred Deconvolutional Network for Building Extraction in Dense Building Areas With Shadow
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Issued Date
2023-01-01
Resource Type
ISSN
19391404
eISSN
21511535
Scopus ID
2-s2.0-85146240129
Journal Title
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume
16
Start Page
773
End Page
786
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol.16 (2023) , 773-786
Suggested Citation
Boonpook W., Tan Y., Torsri K., Kamsing P., Torteeka P., Nardkulpat A. PCL-PTD Net: Parallel Cross-Learning-Based Pixel Transferred Deconvolutional Network for Building Extraction in Dense Building Areas With Shadow. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol.16 (2023) , 773-786. 786. doi:10.1109/JSTARS.2022.3230149 Retrieved from: https://hdl.handle.net/20.500.14740/20888
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Corresponding Author(s)
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Abstract
Urban building segmentation from remote sensed imageries is challenging because there usually exists a variety of building features. Furthermore, very high spatial resolution imagery can provide many details of the urban building, such as styles, small gaps among buildings, building shadows, etc. Hence, satisfactory accuracy in detecting and extracting urban features from highly detailed images still remains. Deep learning semantic segmentation using baseline networks works well on building extraction; however, their ability in building extraction in shadows area, unclear building feature, and narrow gaps among buildings in dense building zone is still limited. In this article, we propose parallel cross-learning-based pixel transferred deconvolutional network (PCL-PTD net), and then is used to segment urban buildings from aerial photographs. The proposed method is evaluated and intercompared with traditional baseline networks. In PCL-PTD net, it is composed of parallel network, cross-learning functions, residual unit in encoder part, and PTD in decoder part. The performance is applied to three datasets (Inria aerial dataset, international society for photogrammetry and remote sensing Potsdam dataset, and UAV building dataset), to evaluate its accuracy and robustness. As a result, we found that PCL-PTD net can improve learning capacities of the supervised learning model in differentiating buildings in dense area and extracting buildings covered by shadows. As compared to the baseline networks, we found that proposed network shows superior performance compared to all eight networks (SegNet, U-net, pyramid scene parsing network, PixelDCL, DeeplabV3+, U-Net++, context feature enhancement networ, and improved ResU-Net). The experiments on three datasets also demonstrate the ability of proposed framework and indicating its performance.