Research ArticleOpen Access

A New Road Extraction Method from Satellite Images Using Genetic Programming

Jing Liang

https://orcid.org/0000-0003-0811-0223

School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, P. R. China

School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang, P. R. China

State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang, P. R. China

E-mail Address: [email protected]

Search for more papers by this author

Yaxin Chang

https://orcid.org/0009-0007-2958-8804

School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, P. R. China

E-mail Address: [email protected]

Search for more papers by this author

Ying Bi

https://orcid.org/0000-0003-2758-6067

School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, P. R. China

State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang, P. R. China

E-mail Address: [email protected]

Corresponding author.

Search for more papers by this author

Caitong Yue

https://orcid.org/0000-0002-3362-0703

School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, P. R. China

State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang, P. R. China

E-mail Address: [email protected]

Search for more papers by this author

Boyang Qu

https://orcid.org/0000-0003-3529-0776

School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou, P. R. China

E-mail Address: [email protected]

Search for more papers by this author

, and

Mengnan Liu

State Key Laboratory of Power System of Tractor, Luoyang, P. R. China

E-mail Address: [email protected]

Search for more papers by this author

https://doi.org/10.1142/S2737480724500092Cited by:0 (Source: Crossref)

Abstract

Extracting roads from satellite images is an important task in the field of computer vision with a wide range of applications. However, efficient road extraction from satellite images remains a complex challenge due to issues such as data labeling and the diversity of road features. Existing methods often struggle to balance accuracy, robustness, and interpretability. Genetic programming (GP) is based on a flexible and interpretable structure that is robust and does not require a large amount of data support. We position the road extraction problem as a binary semantic segmentation task and introduce GP algorithms. First, an approach for extracting pixel neighborhood features is proposed, and features from multiple images in the DeepGlobe road extraction dataset are extracted. Then, an advanced feature construction method based on GP is employed. Finally, these advanced features are utilized for training classifier and classification to achieve road extraction. We have validated the effectiveness of the approach on the DeepGlobe road extraction dataset. The results demonstrated that the proposed approach exhibits superior performance compared to traditional classification methods and multilayer perceptron (MLP) in terms of accuracy, generalization, and interpretability. This study provides a valuable reference for the integration of GP into the domain of road extraction from satellite images, showcasing their potential to enhance the accuracy and efficiency.

Keywords:

References

1. Vitor Oliveira and Paulo Pinho , Evaluation in Urban Planning: Advances and Prospects, J. Plan. Lit. 24(4), 343–361 (2010). Crossref, Google Scholar
2. Francisco Eugenio, J. Martin, Javier Marcello and Eugenio Fraile-Nuez , Environmental Monitoring of El hierro Island Submarine Volcano, by Combining Low and High Resolution Satellite Imagery, Int. J. Appl. Earth Obs. Geoinf. 29, 53–66 (2014). Google Scholar
3. Xiaobin Xu, Yongbin Sun, Haibin Duan, Yimin Deng and Zhigang Zeng , Maritime Target Saliency Detection for UAV based on the Stimulation Competition Selection Mechanism of Raptor Vision, Guidance, Navigation and Control 3(2), 2350012 (2023). Link, Google Scholar
4. Kanchan Bhil, Rithvik Shindihatti, Shifa Mirza, Siddhi Latkar, Y. S. Ingle, N. F. Shaikh, I. Prabu and Satish N. Pardeshi , Recent Progress in Object Detection in Satellite Imagery: A Review, in Sustainable Advanced Computing: Select Proceedings of ICSAC 2021 (Springer, 2022), pp. 209–218. Crossref, Google Scholar
5. Atsushi Higuchi , Toward More Integrated Utilizations of Geostationary Satellite Data for Disaster Management and Risk Mitigation, Remote Sens. 13(8), 1553 (2021). Crossref, Google Scholar
6. Kleio Baxevani, Indrajeet Yadav, Yulin Yang, Michael Sebok, Herbert G. Tanner and Guoquan Huang , Resilient Ground Vehicle Autonomous Navigation in GPS-denied Environments, Guid. Navig. Control 2(4), 2250020 (2022). Link, Google Scholar
7. Jean-Claude Thill , Geographic Information Systems for Transportation in Perspective, Transp. Res. Part C Emerg. Technol. 8(1–6), 3–12 (2000). Crossref, Google Scholar
8. Shunlin Liang and Jindi Wang , Advanced Remote Sensing: Terrestrial Information Extraction and Applications (Academic Press, Cambridge, MA, 2019). Google Scholar
9. Jianxin Jia et al., Review on Active and Passive Remote Sensing Techniques for Road Extraction, Remote Sens. 13(21), 4235 (2021). Crossref, Google Scholar
10. Abolfazl Abdollahi, Biswajeet Pradhan, Nagesh Shukla, Subrata Chakraborty and Abdullah Alamri , Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-of-the-art Review, Remote Sens. 12(9), 1444 (2020). Crossref, Google Scholar
11. Michael O’Neill, Riccardo Poli, William B. Langdon, Nicholas F. Mcphee , A Field Guide to Genetic Programming, Genet. Program. Evol. M. 10, 229–230 (2009). Crossref, Google Scholar
12. Wolfgang Banzhaf, Peter Nordin, Robert E. Keller and Frank D. Francone , Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and its Applications (Morgan Kaufmann Publishers Inc., San Mateo, California, USA, 1998). Google Scholar
13. Pedro G. Espejo, Sebastián Ventura and Francisco Herrera , A Survey on the Application of Genetic Programming to Classification, IEEE Trans. Syst. Man Cybern. Part C 40(2), 121–144 (2009). Crossref, Google Scholar
14. Stefan Sette and Luc Boullart , Genetic Programming: Principles and Applications, Eng. Appl. Artif. Intell. 14(6), 727–736 (2001). Crossref, Google Scholar
15. Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia and Ramesh Raskar , Deepglobe 2018: A Challenge to Parse the Earth Through Satellite Images, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, IEEE Xplore (Computer Vision Foundation) (2018), pp. 172–181. Crossref, Google Scholar
16. Armin Gruen and Haihong Li , Semi-automatic Linear Feature Extraction by Dynamic Programming and lsb-snakes, Photogramm. Eng. Remote Sensing 63(8), 985–994 (1997). Google Scholar
17. Lei Ding, Qimiao Yang, Jun Lu, Junfeng Xu and Jintao Yu , Road Extraction based on Direction Consistency Segmentation, in Pattern Recognition: 7th Chinese Conference, CCPR 2016, November 5–7, Proceedings, Part I (Springer, 2016), pp. 131–144. Crossref, Google Scholar
18. Prajowal Manandhar, Prashanth Reddy Marpu and Zeyar Aung , Segmentation based Traversing-agent Approach for Road Width Extraction from Satellite Images using Volunteered Geographic Information, Appl. Comput. Inform. 17(1), 131–152 (2021). Crossref, Google Scholar
19. Renbao Lian, Weixing Wang, Nadir Mustafa and Liqin Huang , Road Extraction Methods in High-resolution Remote Sensing Images: A Comprehensive Review, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 5489–5507 (2020). Crossref, Google Scholar
20. Weixing Wang, Nan Yang, Yi Zhang, Fengping Wang, Ting Cao and Patrik Eklund , A Review of Road Extraction from Remote Sensing Images, J. Traffic Transp. Eng. (Engl. Ed.) 3(3), 271–282 (2016). Google Scholar
21. Josef Kittler , On the Accuracy of the Sobel Edge Detector, Image Vis. Comput. 1(1), 37–42 (1983). Crossref, Google Scholar
22. Lijun Ding and Ardeshir Goshtasby , On the Canny Edge Detector, Pattern Recognit. 34(3), 721–725 (2001). Crossref, Google Scholar
23. Lei He, Bo Peng, Dan Tang and Yuxia Li , Road Extraction based on Improved Convolutional Neural Networks with Satellite Images, Appl. Sci. 12(21), 10800 (2022). Crossref, Google Scholar
24. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley , Sherjil Ozair, Aaron Courville and Yoshua Bengio, Generative Adversarial Nets, Adv. Neural Inf. Process. Syst. 27, 2014. Google Scholar
25. Qian Shi, Xiaoping Liu and Xia Li , Road Detection from Remote Sensing Images by Generative Adversarial Networks, IEEE Access 6, 25486–25494 (2017). Crossref, Google Scholar
26. Ramesh Kestur, Shariq Farooq, Rameen Abdal, Emad Mehraj, Omkar Narasipura and Meenavathi Mudigere , Ufcn: A Fully Convolutional Neural Network for Road Extraction in rgb Imagery Acquired by Remote Sensing from an Unmanned Aerial Vehicle, J. Appl. Remote Sens. 12(1), 016020–016020 (2018). Crossref, Google Scholar
27. Yongyang Xu, Zhong Xie, Yaxing Feng and Zhanlong Chen , Road Extraction from High-resolution Remote Sensing Imagery using Deep Learning, Remote Sens. 10(9), 1461 (2018). Crossref, Google Scholar
28. Jonathan Long, Evan Shelhamer and Trevor Darrell , Fully Convolutional Networks for Semantic Segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE Xplore (Computer Vision Foundation) (2015), pp. 3431–3440. Crossref, Google Scholar
29. Eason Lin , Comparative Analysis of pix2pix and cyclegan for Image-to-image Translation, Highl. Sci. Eng. Technol. 39, 915–925 (2023). Crossref, Google Scholar
30. Cem Unsalan and Beril Sirmacek , Road Network Detection using Probabilistic and Graph Theoretical Methods, IEEE Trans. Geosci. Remote Sens. 50(11), 4441–4453 (2012). Crossref, Google Scholar
31. Zelang Miao, Bin Wang, Wenzhong Shi and Hua Zhang , A Semi-automatic Method for Road Centerline Extraction from vhr Images, IEEE Geosci. Remote Sens. Lett. 11(11), 1856–1860 (2014). Crossref, Google Scholar
32. Hao Chen, Zhenghong Li, Jiangjiang Wu, Wei Xiong and Chun Du , Semiroadexnet: A Semi-supervised Network for Road Extraction from Remote Sensing Imagery via Adversarial Learning, ISPRS J. Photogramm. Remote Sens. 198, 169–183 (2023). Crossref, Google Scholar
33. Ruyi Liu, Qiguang Miao, Yi Zhang, Maoguo Gong and Pengfei Xu , A Semi-supervised High-level Feature Selection Framework for Road Centerline Extraction, IEEE Geosci. Remote Sens. Lett. 17(5), 894–898 (2019). Crossref, Google Scholar
34. Anchao Yin, Chao Ren, Zhiheng Yan, Xiaoqin Xue, Weiting Yue, Zhenkui Wei, Jieyu Liang, Xudong Zhang and Xiaoqi Lin , Hru-net: High-resolution Remote Sensing Image Road Extraction based on Multi-scale Fusion, Appl. Sci. 13(14), 8237 (2023). Crossref, Google Scholar
35. Christian Ayala, C. Aranda and Mikel Galar , Multi-temporal data augmentation for high frequency satellite imagery: A case study in sentinel-1 and sentinel-2 building and road segmentation, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 43, 25–32 (2022). Crossref, Google Scholar
36. Harith Al-Sahaf, Ying Bi, Qi Chen, Andrew Lensen, Yi Mei, Yanan Sun, Binh Tran, Bing Xue and Mengjie Zhang , A Survey on Evolutionary Machine Learning, J. Roy. Soc. New Zealand 49, 205–228 (2019). Crossref, Google Scholar
37. Miao Lu, Ying Bi, Bing Xue, Qiong Hu, Mengjie Zhang, Yanbing Wei, Peng Yang and Wenbin Wu , Genetic Programming for High-level Feature Learning in Crop Classification, Remote Sens. 14(16), 3982 (2022). Crossref, Google Scholar
38. Caiyun Wen, Miao Lu, Ying Bi, Shengnan Zhang, Bing Xue, Mengjie Zhang, Qingbo Zhou and Wenbin Wu , An Object-based Genetic Programming Approach for Cropland Field Extraction, Remote Sens. 14(5), 1275 (2022). Crossref, Google Scholar
39. P. Mohanaiah, P. Sathyanarayana and L. GuruKumar , Image Texture Feature Extraction using glcm Approach, Int. J. Sci. Res. Publ. 3(5), 1–5 (2013). Google Scholar
40. Robert M. Haralick, Karthikeyan Shanmugam and Its’ Hak Dinstein , Textural Features for Image Classification, IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973). Crossref, Google Scholar
41. Ying Bi, Bing Xue and Mengjie Zhang , Genetic Programming for Image Classification: An Automated Approach to Feature Learning, Vol. 24 (Springer Nature, 2021). Crossref, Google Scholar
42. Mhd Saria Allahham, Tamer Khattab and Amr Mohamed , Deep Learning for rf-based Drone Detection and Identification: A Multi-channel 1-d Convolutional Neural Networks Approach, in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (IEEE, 2020), pp. 112–117. Crossref, Google Scholar
43. Wei Yang, Xiang Zhang, Qian Lei, Dengye Shen, Ping Xiao and Yu Huang , Lane Position Detection based on Long Short-term Memory (lstm), Sensors 20(11), 3115 (2020). Crossref, Google Scholar
44. Fabian Pedregosa et al., Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res. 12, 2825–2830 (2011). Google Scholar
45. Laurensvander Maaten and Geoffrey E. Hinton , Visualizing Data using t-sne, J. Mach. Learn. Res. 9(11) 2579–2605 (2008). Google Scholar