Skip to main content
Log in

Optimized deep networks for road extraction using satellite images

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Nowadays, satellite images are improved rapidly in digital presentations. It is mainly utilized for feature analysis in different applications. Moreover, it has piqued researchers’ interest in road extraction applications. Several works of literature were implemented in the past based on the neural scheme and optimization features because of the complex data. Hence, a novel Antlion-based Tuned Deep Network (ALbTDN) was implemented with essential characteristics. They apply the trained ALbTDN model to unseen satellite images for road extraction. At first, the collected satellite image data was trained to the system, and the loud characteristics were removed in the pre-processing phase. Then, the refined categorization layer imports data for the feature extraction process. Subsequently, the antlion fitness is used to tune the feature extraction variable after extracting the road features based on the derived features’ intensity classification. Later, the extracted features were incorporated into the antlion fitness, which gave better-forecasted results. The proposed method is used to extract roads from satellite imagery accurately. ALbTDN combines the power of deep learning networks with the optimization capabilities of the Antlion algorithm to enhance the road extraction process. Finally, the scheme was implemented through the MATLAB platform. Moreover, the performance metrics were calculated and equated with the other current models. The present suggested model has recorded the highest read extraction performance outcome. Experimental evaluations on benchmark datasets with an effective ALbTDN algorithm, achieving state-of-the-art results in road extraction accuracy and generalization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Jayanthi, S., Vennila, C.: Advanced satellite image classification of various resolution image using a novel approach of deep neural network classifier. Wirel. Pers. Commun. 104, 357–372 (2019). https://doi.org/10.1007/s11277-018-6024-7

    Article  MATH  Google Scholar 

  2. Hariharan, K., Rajaan, N.R., Chelliah, P.P.R., Deepika, M.: The enriched feature enhancement technique for satellite image based on transforms using PCNN. Wirel. Pers. Commun. 117, 2729–2744 (2021). https://doi.org/10.1007/s11277-020-07044-4

    Article  Google Scholar 

  3. Sahoo, R.C., Pradhan, S.K.: An efficient approach for enhancing contrast level and segmenting satellite images: HNN and FCM approach. Wirel. Pers. Commun. 113, 651–667 (2020). https://doi.org/10.1007/s11277-020-07247-9

    Article  MATH  Google Scholar 

  4. Abdollahi, A., Pradhan, B., Shukla, N.: Road extraction from high-resolution orthophoto images using convolutional neural network. J. Indian Soc. Remote Sens. 49(3), 569–583 (2021). https://doi.org/10.1007/s12524-020-01228-y

    Article  Google Scholar 

  5. Jayaseeli, J.D.D., Malathi, D.: An efficient automated road region extraction from high-resolution satellite images using improved cuckoo search with multi-level thresholding schema. Proced. Comput. Sci. 167, 1161–1170 (2020). https://doi.org/10.1016/j.procs.2020.03.418

    Article  MATH  Google Scholar 

  6. Subhashini, D., Srilatha Indira Dutt, V.B.S.: Implementation of satellite road image denoising using iterative domain guided image filtering with gray world optimization. J. Commun. 17, 581–591 (2022). https://doi.org/10.12720/jcm.17.7.581-591

    Article  MATH  Google Scholar 

  7. Nohwal, A., Jangid, T., Panigrahi, N.: Automatic extraction of road network from satellite images of urban areas using convolution neural network. In: Intelligent Infrastructure in Transportation and Management, pp. 181–192. Springer, Singapore (2022)

    Chapter  MATH  Google Scholar 

  8. Xu, Y., Chen, H., Du, C., Li, J.: MSACon: mining spatial attention-based contextual information for road extraction. IEEE Trans. Geosci. Remote Sens. 60, 1–17 (2021). https://doi.org/10.1109/TGRS.2021.3073923

    Article  MATH  Google Scholar 

  9. Yang, C., Wang, Z.: An ensemble Wasserstein generative adversarial network method for road extraction from high-resolution remote sensing images in rural areas. IEEE Access. 8, 174317–174324 (2020). https://doi.org/10.1109/ACCESS.2020.3026084

    Article  Google Scholar 

  10. Zhang, J., Hu, Q., Li, J., Ai, M.: Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 59(3), 1836–1847 (2020). https://doi.org/10.1109/TGRS.2020.3003425

    Article  MATH  Google Scholar 

  11. Yang, B., Wang, S., Zhou, Y., Wang, F., Hu, Q., Chang, Y., Zhao, Q.: Extraction of road blockage information for the Jiuzhaigou earthquake based on a convolution neural network and very-high-resolution satellite images. Earth Sci. Inf. 13(1), 115–127 (2020). https://doi.org/10.1007/s12145-019-00413-z

    Article  MATH  Google Scholar 

  12. Chen, S.B., Ji, Y.X., Tang, J., Luo, B., Wang, W.Q., Lv, K.: DBRANet: road extraction by the dual-branch encoder and regional attention decoder. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021). https://doi.org/10.1109/LGRS.2021.3074524

    Article  Google Scholar 

  13. Salah, M.: Extraction of road centrelines and edge lines from high-resolution satellite imagery using density-oriented Fuzzy C-means and mathematical morphology. J. Indian Soc. Remote Sens. 50, 1243–1255 (2022). https://doi.org/10.1007/s12524-022-01507-w

    Article  MATH  Google Scholar 

  14. Subhashini, D.: A review on road extraction based on neural and non-neural networks. Int. J. Eng. Res. 9(06), 2278 (2020). https://doi.org/10.17577/IJERTV9IS061006

    Article  MATH  Google Scholar 

  15. Zhu, Q., Zhang, Y., Wang, L., Zhong, Y., Guan, Q., Lu, X., Zhang, L., Li, D.: A global context-aware and batch-independent network for road extraction from VHR satellite imagery. ISPRS J. Photogramm. Remote Sens. 175, 353–365 (2021). https://doi.org/10.1016/j.isprsjprs.2021.03.016

    Article  MATH  Google Scholar 

  16. Tao, C., Qi, J., Li, Y., Wang, H., Li, H.: Spatial information inference net: road extraction using road-specific contextual information. ISPRS J. Photogramm. Remote Sens. 158, 155–166 (2019). https://doi.org/10.1016/j.isprsjprs.2019.10.001

    Article  MATH  Google Scholar 

  17. Cheng, G., Wu, C., Huang, Q., Meng, Y., Shi, J., Chen, J., Yan, D.: Recognizing road from satellite images by the structured neural network. Neurocomputing 356, 131–141 (2019). https://doi.org/10.1016/j.neucom.2019.05.007

    Article  MATH  Google Scholar 

  18. Wang, Y., Seo, J., Jeon, T.: NL-LinkNet: toward lighter but more accurate road extraction with nonlocal operations. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022). https://doi.org/10.1109/LGRS.2021.3050477

    Article  Google Scholar 

  19. Hong, M., Guo, J., Dai, Y., Yin, Z.: A novel FMH model for road extraction from high-resolution remote sensing images in urban areas. Proced. Comput. Sci. 147, 49–55 (2019). https://doi.org/10.1016/j.procs.2019.01.183

    Article  Google Scholar 

  20. Punn, N.S., Agarwal, S.: Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. Appl. Intell. 51(5), 2689–2702 (2021). https://doi.org/10.1007/s10489-020-01900-3

    Article  MATH  Google Scholar 

  21. Heidari, A.A., Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M.: Antlion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. In: Nature-inspired optimizers, pp. 23–46. Springer, Cham (2020)

    MATH  Google Scholar 

  22. Shi, Q., Liu, M., Li, S., Liu, X., Wang, F., Zhang, L.: A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2021). https://doi.org/10.1109/TGRS.2021.3085870

    Article  MATH  Google Scholar 

  23. Subhashini, D., Srilatha Indira Dutt, V.B.S.: An innovative hybrid technique for road extraction from noisy satellite images. Mater. Today Proceed. 60, 1229–1233 (2022). https://doi.org/10.1016/j.matpr.2021.08.114

    Article  MATH  Google Scholar 

  24. Zhang, X., Chen, X., Yao, L., Ge, C., Dong, M.: Deep neural network hyper parameter optimization with orthogonal array tuning. In: International conference on neural information processing, pp. 287–295. Springer, Cham (2019)

    Chapter  MATH  Google Scholar 

  25. Kavitha, T.S., Prasad, K.S.: Hybridizing antlion with a whale optimization algorithm for compressed sensing MR image reconstruction via l1 minimization: an ALWOA strategy. Evol. Intel. 14(4), 1985–1995 (2021). https://doi.org/10.1007/s12065-020-00475-9

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

All authors have equal Contribution in this manuscript.

Corresponding author

Correspondence to D. Subhashini.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Subhashini, D., Dutt, V.B.S.S.I. Optimized deep networks for road extraction using satellite images. SIViP 19, 135 (2025). https://doi.org/10.1007/s11760-024-03683-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03683-3

Keywords

Navigation