AUTHORS: Dejan Vukadinov, Raka Jovanovic, Milan Tuba

Download as PDF

ABSTRACT: Monitoring of coastline areas facilitates landscape development, sea transport, sea-level rise, changes in coastal areas and other important activities so it is very important that coastline extraction is quick and precise. In the past coastline extraction consisted of two stages: high-resolution images were captured from airplane and then coastline was drawn based on these images. This operation was slow compared to today’s modern techniques such as satellite images and image processing. In this paper we proposed a new technique for coastline extracting from satellite images. The proposed procedure is based on algorithms for image processing and edge detection. Experimental results showed that the proposed method was fast and accurate.

KEYWORDS: Coastline extraction, histogram matching, Gaussian filter, locally adaptive thresholding, Canny edge detector

REFERENCES:

[1] C. Zhang and Z. Xie, “Combining objectbased texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery,” Remote Sensing of Environment, vol. 124, pp. 310–320, September 2012.

[2] Y.-P. Wang, K.-W. Chang, R.-K. Chen, J.-C. Lo, and Y. Shen, “Large-area rice yield forecasting using satellite imageries,” International Journal of Applied Earth Observation and Geoinformation, vol. 12, no. 1, pp. 27–35, 2010.

[3] X. Li and M. Damen, “Coastline change detection with satellite remote sensing for environmental management of the Pearl River Estuary, China,” Journal of Marine Systems, vol. 82, pp. 54–61, 2010.

[4] R. Aedla, Dwarakish, and V. Reddy, “Automatic shoreline detection and change detection analysis of Netravati-GurpurRivermouth using histogram equalization and adaptive thresholding techniques,” Aquatic Procedia, vol. 4, pp. 563–570, 2015.

[5] H. Demirel, C. Ozcinar, and G. Anbarjafari, “Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition,” IEEE Geoscience and remote sensing letters, vol. 7, no. 2, pp. 333–337, 2009.

[6] S. AL-Mansoori and F. AL-Marzouqi, “Coastline extraction using satellite imagery and image processing techniques,” International Journal of Current Engineering and Technology, vol. 6, pp. 1245–1251, August 2016.

[7] D. Dai and W. Yang, “Satellite image classification via two-layer sparse coding with biased image representation,” IEEE Geoscience and remote sensing letters, vol. 8, no. 1, pp. 173–176, 2011.

[8] M. C. Alonso and J. A. Malpica, “Satellite imagery classification with lidar data,” International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Science, vol. 38, no. 8, pp. 730–735, 2010.

[9] X. Hu, Z. Qin, J. Wang, and G. Su, “Coastline extraction on remote sensing image using Gaussian process classification,” in International Conference on Electromechanical Control Technology, and Transportation, pp. 392–395, 2015.

[10] A. Alshennawy and A. Aly, “Edge detection in digital images using fuzzy logic technique,” International Journal of Computer, Electrical, Automation, Control, and Information Engineering, vol. 3, no. 3, pp. 540–548, 2009.

[11] H. Liu and K. C. Jezek, “Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods,” International journal of remote sensing, vol. 25, pp. 937–958, March 2004.

[12] S. Dellepiane, R. D. Laurentiis, and F. Giordano, “Coastline extraction from SAR images and a method for the evaluation of the coastline precision,” Pattern Recognition Letters, vol. 25, pp. 1461–1470, August 2004.

[13] Z. Liu, F. Li, N. Li, R. Wang, and H. Zhang, “A novel region-merging approach for coastline extraction from Sentinel-1A IW Mode SAR imagery,” IEEE Geoscience and remote sensing letters, vol. 13, pp. 324–328, January 2016.

[14] V. Paravolidakis, K. Moirogiorgou, L. Ragia, M. Zervakis, and C. Synolakis, “Coastline extraction from aerial images based on edge detection,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. III-8, pp. 153–158, July 2016.

[15] T. Zhang, X. Yang, S. Hu, and F. Su, “Extraction of coastline in aquaculture coast from multispectral remote sensing images: Object-based region growing integrating edge detection,” Remote Sensing, vol. 5, pp. 4470– 4487, September 2013.

[16] M. Jordanski, A. Arsic, and M. Tuba, “Dynamic recursive subimage histogram equalization algorithm for image contrast enhancement,” in 23rd Telecommunications Forum Telfor, pp. 819–822, Nov 2015.

[17] M. Tuba, M. Jordanski, and A. Arsic, “Chapter 4 - improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using the bat algorithm,” in BioInspired Computation and Applications in Image Processing (X.-S. Yang and J. P. Papa, eds.), pp. 61–86, Academic Press, 2016.

[18] I. Brajevic and M. Tuba, “Cuckoo search and firefly algorithm applied to multilevel image thresholding,” in Cuckoo Search and Firefly Algorithm: Theory and Applications (X.-S. Yang, ed.), pp. 115–139, Springer International Publishing, 2014.

[19] M. Tuba, “Multilevel image thresholding by nature-inspired algorithms-a short review,” The Computer Science Journal of Moldova, vol. 22, no. 3, pp. 318–338, 2014.

[20] A. Alihodzic and M. Tuba, “Improved bat algorithm applied to multilevel image thresholding,” The Scientific World Journal, vol. 2014, p. 16, 2014.

[21] M. Tuba, N. Bacanin, and A. Alihodzic, “Multilevel image thresholding by fireworks algorithm,” in 25th International Conference Radioelektronika, pp. 326–330, April 2015.