Detection of Road for Landing of Aircraft in an Unfamiliar Environment: A Comparative Study

  • Ali Mobin Memon
  • Imran Amin


This paper is a comparative study about detecting straight road from satellite images. There are multiple applications of road detection. Here, only straight road is considered for use as a landing strip for aircraft emergency landing. To fully guarantee the safe landing of aircraft, multiple criterions are required to be addressed, for example, buildings and traffic. However, the focus of this paper is only to detect straight road from aerial image to ensure it is feasible to land an aircraft or not. If it is feasible, only then the detection process can move forward to the analysis of non-road objects. To find such road, three different image processing methods are used which are (Canny, Sobel and Prewitt), Fuzzy C-Means (FCM) clustering method and Markov Random Field (MRF) classification model. Each method is used to segment the roads from non-road objects. Since, edge detectors and segmentation models may have broken segments morphological operations are applied to join these broken segments, namely dilation and erosion. Then, the Hough transform is applied to detect a straight road. The results obtained were compared and was concluded that canny performed better as compared to other methods used in this comparative study. But practically none of them were found effective enough as straight road detectors. In the end, some issues are addressed and few solutions have been proposed for future work on this paper. 


[1] C. Ni, Q. Ye, B. Li, and S. Zhang, “Road extraction from high-resolution remote sensing image based on phase classification”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, no. 3B, pp: 509-514, 2008.
[2] S.Vijayarani and M.Vinupriya, “Performance Analysis of Canny and Sobel Edge Detection Algorithms in Image Mining”, International Journal of Innovative Research in Computer and Communication Engineering, vol. 1, no. 8, pp. 1760–1767, 2013.
[3] N. S. Kumar, B. Sukanya, B. Mohan, and G. Prathibha, “Extraction of Roads from Satellite Images Based on Edge Detection”, International Journal of Engineering Development and Research, vol. 5, no. 2, pp. 187–190, 2017.
[4] P. Amoako-Yirenkyi, J. K. Appati, and I. K. Dontwi, "Performance Analysis of Image Smoothing Techniques on a New Fractional Convolution Mask for Image Edge Detection", Open Journal of Applied Sciences, vol. 06, no. July, pp. 478–488, 2016.
DOI: 10.4236/ojapps.2016.67048
[5] K. Shah, K. Patel and G. I. Prajapati, “Various Edge Detection Techniques: Survey, Implementation and Comparison”, International Journal of Advanced Research in Computer Science, vol. 4, no. 4, pp. 109-113, 2013.
DOI: 10.26483/ijarcs.v4i4.1603
[6] R. Kumar and Arthanariee A. M., “A Comparative Study of Image Segmentation Using Edge-Based Approach”, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, vol. 7, no. 3, pp. 510–514, 2013.
[7] A. S. Hassanein, S. Mohammad, M. Sameer and M. E. Ragab, “A Survey on Hough Transform, Theory, Techniques and Applications”, International Journal of Computer Science Issues, vol. 12, no. 1, p. 2, 2015.
[8] R. O. Duda and P. E. Hart, “Use of the Hough Transform to Detect Lines and Curves in Pictures,” Communications of the ACM, vol. 15, no. 1, pp. 11–15, 1972.
[9] W. Liu, Z. Zhang, S. Li and D. Tao, “Road Detection by Using a Generalized Hough Transform”, Remote Sensing, vol. 9, no. 6, p. 9(6), 590, 2017.
DOI: 10.3390/rs9060590
[10] N. A. R. Ahmad and P. J. Deore, "Semi-Automatic Road Network Extraction from Satellite Images Using Fuzzy C Means Clustering", International Journal of Computer Applications, pp. 28–30, 2014.
[11] S. Z. Li, "Markov Random Field Models In Computer Vision," in Computer Vision — ECCV '94. ECCV, (Lecture Notes in Computer Science,), Eklundh JO. Eds. Springer, Berlin, Heidelberg, 1994, vol. 801, pp. 361-370.
DOI: 10.1007/BFb0028368
[12] X. Yong, Z. Shaoguang, and X. Yuyue, “Markov Random Field for Road Extraction Applications in Remote Sensing Images”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII, p. B3a, 2008.
[13] GeoEye-1 Satellite Sensor. (2018) [Online]. Available: sensors/geoeye-1/
How to Cite
MEMON, Ali Mobin; AMIN, Imran. Detection of Road for Landing of Aircraft in an Unfamiliar Environment: A Comparative Study. Journal of Independent Studies and Research-Computing, [S.l.], v. 16, n. 1, p. 30-36, june 2018. ISSN 1998-4154. Available at: <>. Date accessed: 17 oct. 2019. doi: