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Cited by 247Remote-Sensing Image ClassificationAdvanced Neural Network ApplicationsAutomated Road and Building Extraction

The DOI matches its Crossref record; 160 of the 166 references it cites were found in a registry or an index.

Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

Open access
Neupane, Bipul, Horanont, Teerayut, Aryal, Jagannath·2021·Remote Sensing
DOI 10.3390/rs13040808
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Abstract

Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this paper is on urban remote sensing images. We review and perform a meta-analysis to juxtapose recent papers in terms of research problems, data source, data preparation methods including pre-processing and augmentation techniques, training details on architectures, backbones, frameworks, optimizers, loss functions and other hyper-parameters and performance comparison. Our detailed review and meta-analysis show that deep learning not only outperforms traditional methods in terms of accuracy, but also addresses several challenges previously faced. Further, we provide future directions of research in this domain.

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160 of 160 references verified0 citation errors · 0 not checkable

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Citations

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Show all 166 references

Research works printed but not linked: 13

  1. Cowen, 1995, The design and implementation of an integrated geographic information system for environmental applications, Photogramm. Eng. Remote Sens., 61, 1393
  2. Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press.
  3. Sherrah, J. (2016). Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv.
  4. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
  5. Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv.
  6. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
  7. Mou, L., and Zhu, X.X. (2018). RiFCN: Recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images. arXiv.
  8. Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv.
  9. Wu, Z., Shen, C., and Hengel, A.V.D. (2016). High-performance semantic segmentation using very deep fully convolutional networks. arXiv.
  10. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, ACM.
  11. Marcu, A., and Leordeanu, M. (2016). Dual local-global contextual pathways for recognition in aerial imagery. arXiv.
  12. Li, P., Lin, Y., and Schultz-Fellenz, E. (2018). Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery. arXiv.
  13. Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv.

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Metadata

Authors
Neupane, B., Horanont, T., & Aryal, J.
Affiliations
  • Advanced Geospatial Technology Research Unit, Sirindhorn International Institute of Technology, 131 Moo 5, Tiwanon Road, Bangkadi, Mueang Pathumthani, Pathumthani 12000, Thailand
  • School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, 131 Moo 5, Tiwanon Road, Bangkadi, Mueang Pathumthani, Pathumthani 12000, Thailand
  • The University of Melbourne (AU)
Corresponding author
Horanont, Teerayut
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2021-02-23
Volume
13
Issue
4
Pages
808
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs13040808
OpenAlex
W3129544296
Cited by
247
References
166
Citations by year
2026: 31 · 2025: 57 · 2024: 60 · 2023: 55 · 2022: 36 · 2021: 7
FWCI
17.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Thammasat University (Thammasat University Research Fund under the TU Research Scholar, Contract No. 6/2562.)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Automated Road and Building Extraction
Keywords
semantic segmentation, urban characteristics, satellite imagery, very high resolution remote sensing, deep learning, data augmentation, data preprocessing, hyperparameters, loss functions, performance comparison

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