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Building Footprint Extraction From Unmanned Aerial Vehicle Images Via PRU-Net: Application to Change Detection
- Source :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 2236-2248 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- 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-Net incorporates pyramid scene parsing (PSP) to allow multiscale scene parsing, a residual block (RB) in ResNet for feature extraction, and focal loss to address sample imbalance. Within the proposed method, building footprint extraction is conducted as follows: 1) unmanned aerial vehicle images are cropped, denoised, and semantically marked, and datasets are created (including training/validation and prediction datasets); 2) the training/validation and prediction datasets are input into the full convolutional neural network PRU-Net for model training/validation and prediction. Compared with the U-Net, PSP+U-Net (PU-Net), and U-Net++ models, PRU-Net offers improved footprint extraction of buildings with a range of sizes and shapes. The large-scale experimental results demonstrated the effectiveness of the PSP module for multiscale scene analysis and the RB module for feature extraction. After demonstrating the improvements in building extraction offered by PRU-Net, the building footprint results were further processed to generate a building change map.
- Subjects :
- Atmospheric Science
010504 meteorology & atmospheric sciences
Computer science
Feature extraction
Geophysics. Cosmic physics
0211 other engineering and technologies
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Residual
01 natural sciences
Convolutional neural network
Footprint
unmanned aerial vehicle (UAV) image
Building footprint change detection
deep convolutional neural network (DCNN)
Pyramid (image processing)
Computers in Earth Sciences
TC1501-1800
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Block (data storage)
business.industry
QC801-809
Pattern recognition
Image segmentation
U-Net
Ocean engineering
Artificial intelligence
business
Change detection
Subjects
Details
- Language :
- English
- ISSN :
- 21511535
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Accession number :
- edsair.doi.dedup.....b8b3975b393cf678e4034f911d30e46e