[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 13, Issue 1 (9-2023) ::
JGST 2023, 13(1): 13-26 Back to browse issues page
A novel deep neural network for multi-scale building extraction from remotely-sensed images
Mohammad Parsaei , Saeid Niazmardi * , Ali Esmaeily
Abstract:   (346 Views)
Building extraction is one of the most crucial requirements of urban planning. Due to their availability and affordable cast, high-resolution remotely sensed images are often used for building extraction. Owing to their impressive performances, Deep learning techniques have attracted the attention of researchers for building extraction from high-resolution images. Nevertheless, most existing models perform poorly in recovering spatial details and discriminating buildings with various sizes and shapes. Hence, this paper proposes an improvement module to address the problems associated with multi-scale building extraction. The proposed module uses dilated convolutions to increase the receiving information area to reduce the discontinuities in the results of large buildings. Extracting large buildings using the proposed module and small buildings using the main architecture of the network has turned the proposed network into an effective method for building extraction. The results of the experiments showed that the proposed module with the IoU of 0.6495 and 0.8572 for Massachusetts and WHU data sets outperformed FCN, U-Net, USSP, and DeepLab V3+. The performance analysis of the proposed module also showed that this module was able to improve the performance of building extraction considering the IoU metric by 0.1077.
Article number: 2
Keywords: Building extraction, neural networks, remote sensing images, deep learning, multi-scale analysis
Full-Text [PDF 1496 kb]   (140 Downloads)    
Type of Study: Research | Subject: Photo&RS
Received: 2022/09/12
References
1. R. Kemker, C. Salvaggio, and C. Kanan, "Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 60-77, 2018/11/01/ 2018, doi: https://doi.org/10.1016/j.isprsjprs.2018.04.014 [DOI:10.1016/j.isprsjprs.2018.04.014.]
2. F. Mohammadimanesh, B. Salehi, M. Mahdianpari, E. Gill, and M. Molinier, "A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 151, 04/02 2019, doi: 10.1016/j.isprsjprs.2019.03.015. [DOI:10.1016/j.isprsjprs.2019.03.015]
3. M. Vakalopoulou, K. Karantzalos, N. Komodakis, and N. Paragios, "Building detection in very high resolution multispectral data with deep learning features," in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 26-31 July 2015 2015, pp. 1873-1876, doi: 10.1109/IGARSS.2015.7326158. [DOI:10.1109/IGARSS.2015.7326158]
4. X. Tong et al., "Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 79, pp. 53-67, May 01, 2013 2013, doi: 10.1016/j.isprsjprs.2013.01.012. [DOI:10.1016/j.isprsjprs.2013.01.012]
5. M. Rezaei, H. Arefi, H. Rastiveis, and M. Sajadian, "Building Extraction and Modeling Using LiDAR Point Clouds Imaging on Two-Dimensional Surface," (in eng), Journal of Geomatics Science and Technology, Research vol. 7, no. 3, pp. 139-150, 2018. [Online]. Available: http://jgst.issge.ir/article-1-361-fa.html.
6. M. Khoshboresh Masouleh, R. Shah-Hosseini, and A. R. Safari, "Integration of Deep Learning Algorithms and Bilateral Filters with the Purpose of Building Extraction from Mono Optical Aerial Imagery," (in eng), Journal of Geospatial Information Technology, Research vol. 7, no. 2, pp. 241-263, 2019, doi: 10.29252/jgit.7.2.241. [DOI:10.29252/jgit.7.2.241]
7. R. Yazdan, M. J. Valadan Zoej, H. Ebadi, and A. Mohammadzadeh, "Semi-Automatic Building Extraction Using Snake Models from High Resolution Aerial Images," (in 2), Journal of Geomatics Science and Technology, Research vol. 4, no. 2, pp. 179-188, 2014. [Online]. Available: http://jgst.issge.ir/article-1-253-fa.html.
8. D. Tiede, G. Schwendemann, A. Alobaidi, L. Wendt, and S. Lang, "Mask R‐CNN‐based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid‐19 response in Khartoum, Sudan," Transactions in GIS, vol. 25, 05/01 2021, doi: 10.1111/tgis.12766. [DOI:10.1111/tgis.12766]
9. K. Karantzalos and N. Paragios, "Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection," Geoscience and Remote Sensing, IEEE Transactions on, vol. 47, pp. 133-144, 02/01 2009, doi: 10.1109/TGRS.2008.2002027. [DOI:10.1109/TGRS.2008.2002027]
10. K. Khoshelham and Z. Li, "A model - based approach to semi - automated reconstruction of buildings from aerial images," (in en), Photogrammetric record, vol. 19, no. 108, pp. 342 - 359, 2004 2004, doi: urn:nbn:nl:ui:28-1530137e-e9f8-43df-8885-f3ea99f3a19b. [DOI:10.1111/j.0031-868X.2004.00290.x]
11. G. Sohn and I. Dowman, "Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 62, no. 1, pp. 43-63, 2007/05/01/ 2007, doi: https://doi.org/10.1016/j.isprsjprs.2007.01.001 [DOI:10.1016/j.isprsjprs.2007.01.001.]
12. Z. J. Liu, J. Wang, and W. P. Liu, "Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform," in Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 29-29 July 2005 2005, vol. 4, pp. 2250-2253, doi: 10.1109/IGARSS.2005.1525421. [DOI:10.1109/IGARSS.2005.1525421]
13. R. Attarzadeh and M. Momeni, "Object-Based Building Extraction from High Resolution Satellite Imagery," Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XXXIX-B4, pp. 57-60, 2012, doi: 10.5194/isprsarchives-XXXIX-B4-57-2012. [DOI:10.5194/isprsarchives-XXXIX-B4-57-2012]
14. S. Cui, Q. Yan, and P. Reinartz, "Complex building description and extraction based on Hough transformation and cycle detection," Remote Sensing Letters, vol. 3, no. 2, pp. 151-159, 2012/03/01 2012, doi: 10.1080/01431161.2010.548410. [DOI:10.1080/01431161.2010.548410]
15. X. Huang and L. Zhang, "Morphological Building/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 1, pp. 161-172, 2012, doi: 10.1109/JSTARS.2011.2168195. [DOI:10.1109/JSTARS.2011.2168195]
16. Y. Liu, L. Gross, Z. Li, X. Li, X. Fan, and W. Qi, "Automatic Building Extraction on High-Resolution Remote Sensing Imagery Using Deep Convolutional Encoder-Decoder With Spatial Pyramid Pooling," IEEE Access, vol. 7, pp. 128774-128786, 2019, doi: 10.1109/ACCESS.2019.2940527. [DOI:10.1109/ACCESS.2019.2940527]
17. Z. Ye, Y. Fu, M. Gan, J. Deng, A. Comber, and K. Wang, "Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network," Remote Sensing, vol. 11, no. 24, p. 2970, 2019. [Online]. Available: https://www.mdpi.com/2072-4292/11/24/2970. [DOI:10.3390/rs11242970]
18. W. Kang, Y. Xiang, F. Wang, and H. You, "EU-Net: An Efficient Fully Convolutional Network for Building Extraction from Optical Remote Sensing Images," Remote Sensing, vol. 11, no. 23, p. 2813, 2019. [Online]. Available: https://www.mdpi.com/2072-4292/11/23/2813. [DOI:10.3390/rs11232813]
19. S. Ji, S. Wei, and M. Lu, "Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 1, pp. 574-586, 2019, doi: 10.1109/TGRS.2018.2858817. [DOI:10.1109/TGRS.2018.2858817]
20. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs," IEEE transactions on pattern analysis and machine intelligence, vol. 40, no. 4, pp. 834-848, 2017. [DOI:10.1109/TPAMI.2017.2699184]
21. B. Artacho and A. Savakis, "Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation," Sensors, vol. 19, no. 24, p. 5361, 2019. [Online]. Available: https://www.mdpi.com/1424-8220/19/24/5361. [DOI:10.3390/s19245361]
22. Q. Zhu, C. Liao, H. Hu, X. Mei, and H. Li, "MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction From Remote Sensed Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 7, pp. 6169-6181, 2021, doi: 10.1109/TGRS.2020.3026051. [DOI:10.1109/TGRS.2020.3026051]
23. J. Cai and Y. Chen, "MHA-Net: Multipath Hybrid Attention Network for Building Footprint Extraction From High-Resolution Remote Sensing Imagery," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 5807-5817, 2021, doi: 10.1109/JSTARS.2021.3084805. [DOI:10.1109/JSTARS.2021.3084805]
24. J. Ma, L. Wu, X. Tang, F. Liu, X. Zhang, and L. Jiao, "Building Extraction of Aerial Images by a Global and Multi-Scale Encoder-Decoder Network," Remote Sensing, vol. 12, no. 15, p. 2350, 2020. [Online]. Available: https://www.mdpi.com/2072-4292/12/15/2350. [DOI:10.3390/rs12152350]
25. F. Yu and V. Koltun, "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv:1511.07122, 2015.
26. G. E. Hinton and V. Mnih, "Machine learning for aerial image labeling," 2013.
27. O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Cham, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds., 2015// 2015: Springer International Publishing, pp. 234-241. [DOI:10.1007/978-3-319-24574-4_28]
28. Z. Zhang, "Improved Adam Optimizer for Deep Neural Networks," in 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), 4-6 June 2018 2018, pp. 1-2, doi: 10.1109/IWQoS.2018.8624183. [DOI:10.1109/IWQoS.2018.8624183]
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Parsaei M, Niazmardi S, Esmaeily A. A novel deep neural network for multi-scale building extraction from remotely-sensed images. JGST 2023; 13 (1) : 2
URL: http://jgst.issgeac.ir/article-1-1111-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 13, Issue 1 (9-2023) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology