Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/29530
ชื่อเรื่อง: PCL-PTD Net: Parallel Cross-Learning-Based Pixel Transferred Deconvolutional Network for Building Extraction in Dense Building Areas With Shadow
ผู้แต่ง: Boonpook W.
Tan Y.
Torsri K.
Kamsing P.
Torteeka P.
Nardkulpat A.
Keywords: Building extraction
building shadow
dense building
PCL-PTD net
semantic segmentation
วันที่เผยแพร่: 2023
สำนักพิมพ์: Institute of Electrical and Electronics Engineers Inc.
บทคัดย่อ: 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. © 2008-2012 IEEE.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146240129&doi=10.1109%2fJSTARS.2022.3230149&partnerID=40&md5=84763a48d32130f1436c6fa3d9ab1e1f
https://ir.swu.ac.th/jspui/handle/123456789/29530
Appears in Collections:Scopus 2023

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