Advertisement

Cluster Computing

, Volume 22, Supplement 5, pp 11891–11898 | Cite as

Edge detection of satellite image using fuzzy logic

  • R. DhivyaEmail author
  • R. Prakash
Article

Abstract

Edge detection plays an important role in the field of image processing. Various edge detection techniques are obtained like Sobel, PSO Preweitt, Laplacian and Laplacian of Gaussian. These techniques consume some restrictions such as fixed edge thickness and some parameter like threshold is problematic to implement. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. High performance computing (HPC) is a technique used to assemble process and analyze more quantity of remote sensing (satellite) data which needs less processing time. HPC method is used in distributed & cluster Computing, parallel computing and following satellite data analyzing & processing approaches like geo-referencing, image edge detection, image classification, image mosaicking, Morphological/neural approach and image fusion for hyperspectral satellite data. Stimulated by means of the specific clustering of theory called gravitational theory, an original edge detection process. The conservative methods include the usage of linear time invariant filters. Recognition of edge is done by these filters as a These filters recognize an edge as a sudden conversion of grey scale pixel intensity. These techniques are well recognized and computationally effective. Canny, Sobel, Robert, Kirsch, Prewitt and LOG, on idea of spatial differential filters by using local grade. Within the less time these filters process the data and are inclined to noise. But in fuzzy technique, it does not include like this restriction, by altering the rules and output parameters, it is possible to change the thickness of the edge. In this paper, a new technique is created on fuzzy logic, which is suggested for detection of edge in digital images deprived of finding the threshold value. This method starts through segmenting the images into sections by means of fluctuating 3 \(\times \) 3 binary matrixes. The pixels of edge are planned to a range of standards separated from each other. The results of this proposed technique is compared with the results obtained from PSO and neural network methods. This suggested technique provides a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. And sharpness in the curves.

Keywords

Hyperspectral images High performance computing PSO Neural edge detection Fuzzy logic 

References

  1. 1.
    Verma, O.P., Parihar, A.S.: An optimal fuzzy system for edge detection in color images using bacterial foraging algorithm. IEEE Trans. Fuzzy Syst. 25(1), 114–127 (2017)CrossRefGoogle Scholar
  2. 2.
    Solanki, C., Godfrey, W.W.: Technique for edge detection based on interval Type-2 fuzzy logic with sobel filtering. In: International Conference on Information Science (ICIS), pp. 38–43 (2016)Google Scholar
  3. 3.
    Patel, D.K., More, S.A.: Technique by fuzzy logic and cellular learning automata using fuzzy image processing. In: International Conference on Computer Communication and Informatics, pp. 1–6 (2013)Google Scholar
  4. 4.
    Kumar, E.B., Sundaresan, M.: Edge detection using trapezoidal membership function based on fuzzy’s mamdani inference system. In: International Conference on Computing for Sustainable Global Development (INDIACom), pp. 515–518 (2014)Google Scholar
  5. 5.
    Sarakon, P., Charoensiriwath, S., Uyyanonvara, B., Kaneko, H.: 3D body shape clustering based on PSO by multi-fitness function. In: IEEE Conference on Knowledge and Smart Technology (KST), pp. 34–39 (2017)Google Scholar
  6. 6.
    Kumar, V., Lal, T., Dhuliya, P., Pant, D.: A study and comparison of different image segmentation algorithms. In: 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA), pp. 1–6 (2016)Google Scholar
  7. 7.
    Gautam, D., Ahmed, M.: Efficient fuzzy edge detection using successive Otsu’s method. In: International Conference for Convergence for Technology, pp. 1–5 (2014)Google Scholar
  8. 8.
    Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R., Woodcock, C.E.: Landsat continuity: issues and opportunities for land cover monitoring. Remote Sens. Environ. 112, 955–969 (2008)CrossRefGoogle Scholar
  9. 9.
    Fergani, B., Kholladi, M.K.: A discrete particle swarm optimization algorithm for geographical map contour reconstruction. In: IEEE Conference on Digital Information and Communication Technology and its Applications (DICTAP), pp. 142–144 (2016)Google Scholar
  10. 10.
    Chen, L., Ma, Y., Liu, P., Wei, J., Jie, W., He, J.: A review of parallel computing for large-scale remote sensing image mosaicking. Clust. Comput. 18(2), 517–529 (2015)CrossRefGoogle Scholar
  11. 11.
    Chinnasamy, S.: Performance improvement of fuzzy-based algorithms for medical imageretrieval. IET Image Process. 8(6), 319–326 (2014)CrossRefGoogle Scholar
  12. 12.
    Mathur, N., Dadheech, P., Gupta, M.K.: The K-means Clustering based fuzzy edge detection technique on MRI images. Fifth International Conference on Advances in Computing and Communications (ICACC), pp. 330–333 (2015)Google Scholar
  13. 13.
    Melin, P., Gonzalez, C.I., Castro, J.R., Mendoza, O., Castillo, O.: Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)CrossRefGoogle Scholar
  14. 14.
    Codizar, A.L., Solano, G.: Plant leaf recognition by venation and shape using artificial neural networks. In: International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–4 (2016)Google Scholar
  15. 15.
    Homer, C.G., Ramsey, R.D., Edwards, T.C., Falconer, A.: Landscape cover-type modeling using a multi-scene thematic mapper mosaic. Photogramm. Eng. Rem. S. 63, 59–67 (1997)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Kingston Engineering CollegeVelloreIndia
  2. 2.Vivekananda College of Engineering for WomenTiruchengodeIndia

Personalised recommendations