Building Footprint Extraction From Unmanned Aerial Vehicle Images Via PRU-Net: Application to Change Detection

As the manual detection of building footprint is inefficient and labor-intensive, this study proposed a method of building footprint extraction and change detection based on deep convolutional neural networks. The study modified the existing U-Net model to develop the "PRU-Net" model. PRU-...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.2236-2248
Hauptverfasser:	Liu, Wei, Xu, Jiawei, Guo, Zihui, Li, Erzhu, Li, Xing, Zhang, Lianpeng, Liu, Wensong
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Building footprint change detection Buildings Change detection Data mining Datasets deep convolutional neural network (DCNN) Detection Feature extraction Image segmentation Labour Licenses Modules Neural networks Noise reduction Predictions Predictive models Scene analysis Semantics Training U-Net unmanned aerial vehicle (UAV) image Unmanned aerial vehicles
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