Publication: Deep learning-based multi-feature semantic segmentation in building extraction from images of UAV photogrammetry
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Issued Date
2021
Resource Type
File Type
application/pdf
ISSN
1431161
Other identifier(s)
2-s2.0-85089520833
Rights
Srinakharinwirot University
Rights Holder(s)
Scopus
Bibliographic Citation
International Journal of Remote Sensing. Vol 42, No.1 (2021), p.1-19
Suggested Citation
Boonpook W., Tan Y., Xu B. Deep learning-based multi-feature semantic segmentation in building extraction from images of UAV photogrammetry. International Journal of Remote Sensing. Vol 42, No.1 (2021), p.1-19. doi:10.1080/01431161.2020.1788742 Retrieved from: https://hdl.handle.net/20.500.14740/4489
Author(s)
Abstract
Building information is an essential part of geographic information system (GIS) applications in urban planning and management. However, it changes rapidly with economic growth. Unmanned aerial vehicles (UAV)-based photogrammetry works well in this situation with its advantages of quick and high-resolution data updating. In this paper, in order to improve building extraction accuracy in complex areas where buildings are characterized by various patterns, complex structures, and unique styles, we present a framework which applies deep learning (DL) semantic segmentation to UAV images with digital surface model (DSM) and visible-band difference vegetation index (VDVI). The results show that extraction accuracy improves. The combination of red, green, blue (RGB) and VDVI bands (RGBVI) can effectively distinguish the building area and vegetation. The application of RGB with DSM bands (RGBD) helps separate buildings from ground objects. The combination of RGB, DSM, and VDVI bands (RGBDVI) can identify small buildings which are usually not high and covered partly by tree branches. The proposed method is further applied to an open standard dataset to evaluate its robustness and results indicate an increased overall accuracy from RGB only (93%) to RGBD (97%). © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Subject(s)
Antennas
Buildings
Economics
Extraction
Image enhancement
Image segmentation
Information management
Photogrammetry
Semantics
Unmanned aerial vehicles (UAV)
Vegetation
Building extraction
Complex structure
Digital surface models
Economic growths
Extraction accuracy
High resolution data
Overall accuracies
Semantic segmentation
Deep learning
Accuracy assessment
Building
GIS
Machine learning
Photogrammetry
Remote sensing
Segmentation
Urban planning
Vegetation index
Buildings
Economics
Extraction
Image enhancement
Image segmentation
Information management
Photogrammetry
Semantics
Unmanned aerial vehicles (UAV)
Vegetation
Building extraction
Complex structure
Digital surface models
Economic growths
Extraction accuracy
High resolution data
Overall accuracies
Semantic segmentation
Deep learning
Accuracy assessment
Building
GIS
Machine learning
Photogrammetry
Remote sensing
Segmentation
Urban planning
Vegetation index