EDGE DETECTION BASED ON ANT COLONY OPTIMIZATION IN SAR IMAGES
Object/Target detection is difficult to process due to speckles in SAR images, which provide high radiometric and geometric resolution independent of all atmospheric conditions. By using edge detection method which extracts important information in the image, it is possible to obtain higher accuracy and less processing SAR image for target detection by eliminating these speckles. The ant colony algorithm, which is one of the heuristic optimization methods, is an algorithm based on mathematical models of real ant colony behaviors. In image processing area, Ant Colony Optimization (ACO) provides an effective contribution in some methods such as object/target detection in specific images by using edge detection technique. We aim to eliminate the speckles that make difficult for target detection in SAR images by using Edge Detection based on Ant Colony Optimization, which is an effective optimization method.
Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., Papathanassiou, K. P., “A tutorial on synthetic aperture radar”, IEEE Geosci. Remote Sensing Mag., 1 (1): 6-43 (2013).
Frei, W., Chen, C., "Fast Boundary Detection: A Generalization and New Algorithm," IEEE Trans. Computers, vol. C-26, no. 10, pp. 988-998, Oct. 1977.
Canny, J., “A computational approach to edge detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698, Nov. 1986.
Muthukrishnan, R., Radha, M., “Edge Detectıon Techniques for Image Segmentation” International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 6, Dec 2011
Nunes de Castro, L., “Fundamentals of Nature Computing: an Overview” Elsevier Physics of Life Reviews 4 1-36, 2007.
Nunes de Castro, L., Natural computing. In: Khosrow-Pour M, editor. Encyclopedia of information science and technology, vol. IV. Idea Group Inc; 2005. p. 2080–4.
Nunes de Castro, L., Von Zuben, F.J., Recent developments in biologically inspired computing. Idea Group Publishing; 2004.
Dorigo, M., “Optimization, learning and natural algorithms” Ph.D. thesis, Dipartimento di Eletronica, Politecnico di Milano, Italy, 1992.
Feng, W., Zhang, J., Miao, Q., “Evaluation of several typical edge detection operators” in Electronic Design Engineering. 2011,19(4), pp. 131- 133.
Baterina, A.V., Oppus,C., “Image Edge Detection Using Ant Colony Optimization”, WSEAS Transactions on Signal Processing, Issue 2, Volume 6, April 2010.
Li, L., Wang, J. “SAR Image Ship Detection Based on Ant Colony Optimization” in 2012 5th International Congress on Image and Signal Processing (CISP 2012).
Tian, J., Yu, W., Sheng, X., “An Ant Colony Optimization Algorithm for Image Edge Detection” in IEEE Congress on Evolutionary Computation. 1(6),2008,pp.751-756.
Otsu, N., “A threshold selection method from gray-level histograms” in IEEE Trans.Syst.1979(9),pp.62-66.
Schou, J., Skriver, H., Nielsen, A., Conradsen, K., “CFAR Edge Detector for Polarimetric SAR Images.”IEEE Transactions on Geoscience and Remote Sensing, 41(1), 20-32,(2002).