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Deep Convolutional Encoder–Decoder Models for Road Extraction from Aerial Imagery

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ICT: Innovation and Computing (ICTCS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 879))

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Abstract

Road extraction from aerial imagery is not a trivial task. It plays a pivotal role in urban planning, navigation, disaster assessment and various other fields. It poses challenges due to complex scenarios and factors, including occlusion. Hence conventional methods prove to be inefficient for the purpose. Image segmentation and deep learning models are extensively employed in recent times to extract objects from images. In this paper, the performance of Unet architecture-based model has been improved by Resnet50, VGG16, DenseNet169, Xception and Efficientnet-b4. Further, to investigate the performance of Unet model, three other models FPN, PSPNet and PAN were implemented and evaluated on Massachusetts road dataset. The work presents the comparative analyses of the performance of models.

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References

  1. Sujatha C, Selvathi D (2015) Connected component-based technique for automatic extraction of road centreline in high resolution satellite images. EURASIP J Image Video Process 2015(1):8

    Article  Google Scholar 

  2. Alshehhi R, Marpu PR (2017) Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images. ISPRS J Photogramm Remote Sens 126:245–260

    Article  Google Scholar 

  3. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556

  4. Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv 2015, arXiv:1511.07122

  5. Zhou L, Zhang C, Wu M (1997) D-linknet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: Proceedings of the IEEE conference on computer vision and pattern recognition work-shops, San Juan, PR, USA, 17–19 June 1997, pp 182–186

    Google Scholar 

  6. Chaurasia A, Culurciello E (2017) LinkNet: exploiting encoder representations for efficient semantic segmentation. arXiv 2017, arXiv:1707.03718v1

  7. Zhou M, Sui H, Chen S, Wang J, Chen X (2020) BT-RoadNet: a boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery. ISPRS J Photogramm Remote Sens 168:288–306

    Google Scholar 

  8. Chen Z, Wang C, Li J, Xie N, Han Y, Du J (2021) Reconstruction bias U-Net for road extraction from optical remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 14:2284–2294

    Google Scholar 

  9. Dey MS, Chaudhuri U, Banerjee B, Bhattacharya A (2021) Dual-path morph-UNet for road and building segmentation from satellite images. IEEE Geosci Remote Sens Lett 19:1–5

    Google Scholar 

  10. Zheng W, Tian X, Yang B, Liu S, Ding Y, Tian J, Yin L (2022) A few shot classification methods based on multiscale relational networks. Appl Sci 12:4059

    Google Scholar 

  11. Geng Q, Zhang H, Qi X, Huang G, Yang R, Zhou Z (2021) Gated path selection network for semantic segmentation. IEEE Trans Image Process 30:2436–2449

    Google Scholar 

  12. Yuan Q, Shen H, Li T et al (2020) Deep learning in environmental remote sensing: achievements and challenges. Remote Sens Environ 241, Article ID 111716

    Google Scholar 

  13. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444; Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the international conference on learning representations, San Diego, CA, USA, 7–9 May 2015

    Google Scholar 

  14. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Los Alamitos, CA, USA, 27–30 June 2016, pp 770–778

    Google Scholar 

  15. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the 30th IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, 21–26 July 2017, pp 2261–2269

    Google Scholar 

  16. Mnih V, Hinton GE (2010) Learning to detect roads in high-resolution aerial images. In: Proceedings of the European conference on computer vision, Heraklion, Crete, Greece, 5–11 Sept 2010, pp 210–223

    Google Scholar 

  17. Mnih V (2013) Machine learning for aerial image labeling. Ph.D. thesis, University of Toronto, Toronto, ON, Canada

    Google Scholar 

  18. Wang J, Song J, Chen M, Yang Z (2015) Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine. Int J Remote Sens 36:3144–3169

    Google Scholar 

  19. Alshehhi R, Marpu PR, Woon WL, Mura MD (2017) Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens 130:139–149

    Google Scholar 

  20. Rezaee M, Zhang Y (2017) Road detection using deep neural network in high spatial resolution images. In: Proceedings of the joint urban remote sensing event (JURSE 2017), Dubai, United Arab Emirates, 6–8 Mar 2017, pp 1–4

    Google Scholar 

  21. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 7–12 June 2015, pp 3431–3440

    Google Scholar 

  22. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the medical image computing and computer-assisted intervention, Munich, Germany, 5–9 Oct 2015, pp 234–241

    Google Scholar 

  23. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481–2495

    Google Scholar 

  24. Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv 2017, arXiv:1706.05587

  25. Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv 2018, arXiv:1802.02611

  26. Gao L, Song W, Dai J, Chen Y (2019) Road extraction from high resolution remote sensing imagery using refined deep residual convolutional neural network. Remote Sens. (ii):1–16

    Google Scholar 

  27. Mnih V (2013) Machine learning for aerial image labeling. Toronto

    Google Scholar 

  28. https://towardsdatascience.com/unet-line-by-line-explanation-9b191c76baf5

  29. Li T, Comer M, Zerubia J (2019) Feature extraction and tracking of CNN segmentations for improved road detection from satellite imagery. In: ICIP 2019—IEEE international conference on image processing, Sept 2019, Taipei, Taiwan. ffhal-01813781v2f

    Google Scholar 

  30. Ye L, Wang L, Zhang W, Li Y, Wang Z (2019) Deep metric learning method for high resolution remote sensing image scene classification 48(6):698

    Google Scholar 

  31. Liu Y, Minh Nguyen D, Deligiannis N, Ding W, Munteanu AJRS (2017) Hourglass-ShapeNetwork based semantic segmentation for high resolution aerial imagery. Remote Sens 9(6):522

    Google Scholar 

  32. Hamaguchi R, Fujita A, Nemoto K, Imaizumi T, Hikosaka S (2018) Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery. In: Proceedings of the 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, Lake Tahoe, Nevada, USA, Mar 2018, pp 1442–1450

    Google Scholar 

  33. Wang H, Wang Y, Zhang Q, Xiang S, Pan CJRS (2017) Gated convolutional neural network for semantic segmentation in high-resolution images. Remote Sens 9(5):446

    Article  Google Scholar 

  34. Shang R, Zhang J, Jiao L, Li Y, Marturi N, Stolkin RJRS (2020) Multi-scale adaptive feature fusion network for semantic segmentation in remote sensing images. Remote Sens 12(5):872

    Article  Google Scholar 

  35. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, July 2017, pp 1442–1450

    Google Scholar 

  36. Chaurasia A, Culurciello E (2017) LinkNet: exploiting encoder representations for efficient semantic segmentation. In: Proceedings of the 2017 IEEE visual communications and image processing (VCIP). IEEE, Petersburg, FL, USA, Dec 2017, pp 1–4

    Google Scholar 

  37. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, July 2017, pp 2117–2125

    Google Scholar 

  38. https://www.kaggle.com/datasets/balraj98/massachusetts-roads-dataset

  39. https://medium.com/@dhanush.patel/imagesegmentation-6950eb534d05

  40. Russakovsky O, Deng J, Su H et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252

    Article  MathSciNet  Google Scholar 

  41. https://segmentation-modelspytorch.readthedocs.io/en/latest/

  42. Abdollahi A, Pradhan B, Shukla N, Chakraborty S, Alamri AM (2020) Deep learning approaches applied to remote sensing datasets for road extraction: a state-of-the-art review. Remote Sens 12:1444

    Google Scholar 

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Author information

Authors and Affiliations

  1. ITS Engineering College, Greater Noida, India

    Ashish Kumar

  2. Meerut Institute of Technology, Meerut, India

    M. Izharul Hasan Ansari

  3. Manipal University Jaipur, Jaipur, India

    Amit Garg

Authors
  1. Ashish Kumar
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  2. M. Izharul Hasan Ansari
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  3. Amit Garg
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Corresponding author

Correspondence to Amit Garg .

Editor information

Editors and Affiliations

  1. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

  2. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  3. University of Peradeniya, Kandy, Sri Lanka

    Roshan G. Ragel

  4. Department of Comp Sci & Engg, SNS College of Technology, Coimbatore, Tamil Nadu, India

    S. Karthik

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Kumar, A., Izharul Hasan Ansari, M., Garg, A. (2024). Deep Convolutional Encoder–Decoder Models for Road Extraction from Aerial Imagery. In: Joshi, A., Mahmud, M., Ragel, R.G., Karthik, S. (eds) ICT: Innovation and Computing. ICTCS 2023. Lecture Notes in Networks and Systems, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_1

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  • DOI: https://doi.org/10.1007/978-981-99-9486-1_1

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  • Online ISBN: 978-981-99-9486-1

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