Abstract

In remote sensing image analysis, segmentation of an image is an important aspect. It classifies similar pixels within the image. Image Segmentation is helpful in analyzing the patterns, objects, and edges within an image. There are many ways for performing image segmentation. In this paper, we are segmenting a satellite image using Multiphase Chan-Vese model. Chan-Vese models are based on ‘Active Contours without edges’. Active contour model is also known as Snake and Energy-Based Model, which is finding local minima in the equivalent energy function. Chan-Vese model gives effective results of segmented image. The multiphase level set construction is mechanized to avoid the drawback of overlap and vacuum; it can also signify edges with convoluted topologies. Researchers conclude in this paper with the findings that the multiphase CV method can give a sensible segmented image of satellite imagery with 2D-DWT, when they manipulate Heaviside function.

References

• Y. Z. ,. M. Z. V. Dey, "A REVIEW ON IMAGE SEGMENTATION TECHNIQUES WITH REMOTE SENSING PERSPECTIVE," ISPRS,, vol. 38, no. 7, pp. 1-12, 2010.
• W. a. D. T. Michael Kass, "Snakes: Active contour models," International Journal of Computer vision, vol. 1, no. 4, pp. 321-331, January 1988.
• L. A. V. Tony F. Chan, "Active contours without edges," IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266 - 277, Feb 2001 .
• T. F. C. Luminita A. Vese, "A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model," International Journal of Computer Vision, vol. 50, no. 3, p. 271–293, December 2002.
• J. S. David Mumford, "Optimal approximations by piecewise smooth functions and associated variational problems," Communications on Pure and Applied Mathematics, July 1989.
• J. Shah, "Riemannian Drums, Anisotropic Curve Evolution and Segmentation," International Conference on Scale-Space Theories in Computer Vision, vol. 1682, pp. 129-140, 12 April 2002.
• G. Gonthier, "Formal Proof—The Four Color Theorem," AMS, December 2008. [Online]. Available: http://www.ams.org/notices/200811/tx081101382p.pdf.
• J. A. S. STANLEY OSHER, "Fronts Propagating with Curvature- Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations," JOURNAL OF COMPUTATIONAL PHYSICS, vol. 79, no. 1, pp. 12-49, 1988.
• R. P. F. Stanley Osher, "Level set methods: An overview and some recent results.," Journal of Computational Physics, vol. 169, no. 2, pp. 463 - 502, 30 May 2001.
• E. W. Weisstein, "Heaviside Step Function.," MathWorld--A Wolfram Web Resource, [Online]. Available: http://mathworld.wolfram.com/HeavisideStepFunction.html.
• J. Shah, "A common framework for curve evolution, segmentation and anisotropic diffusion," in Computer Vision and Pattern Recognition, 1996. Proceedings CVPR '96, 1996 IEEE Computer Society Conference, San Francisco, CA, USA,, 18-20 June 1996.
• ENVI Software, Exelis Visual Information Solutions.
• R. S. Kriti Bajpai, "Analysis of Image Enhancement Techniques Used in Remote Sensing Satellite Imagery," International Journal of Computer Application, vol. 169, no. 10, pp. 1-11, 20 July 2017.
• A. P. Unni, "Satellite Image Enhancement Using 2D Level DWT," International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 3, pp. 1926-1929, March 2014.
• Mathwork, "Matlab Documantation," [Online]. Available: https://in.mathworks.com/help/matlab/examples.html.