Computer Science ›› 2017, Vol. 44 ›› Issue (12): 310-315.doi: 10.11896/j.issn.1002-137X.2017.12.056

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Application Research on Split Bregman Algorithm in Edge Detection of Remote Sensing Image

JING Yu, LIU Jian-xin, LIU Zhao-xia and LI Shao-hua   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Considering the drawbacks of the edge detection method based on the level set,such as weak anti-noise performance,weak capability of dealing with weak edge boundaries and intensity inhomogeneity,lower computational efficiency, the accuracy of edge detection results depends greatly on the location of the initial contour,and curve evloution is easy to get into minimal value.This paper presented an edge detection method based on the global optimal convex function variational model and Split Bregman number minimization.The proposed algorithm constructs a generalized convex function variational model which can get the global optimal solution,according to the principle of CV model and Chan’s global optimization idea.In the process of the active contour evolving toward object boundaries and numerical minimization,a fast iterative algorithm based on Split Bregman is used for overcoming drawbacks of noise and others.Finally,the curve can evolve to the target boundaries quickly and accurately.Experimental results show that the proposed edge detection method has higher computational efficiency and can meet the real-time requirements of remote sensing image,and also has higher precision and better universality.

Key words: Edge detection,Active contour model,Remote sensing image,Split Bregman algorithm

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