Split Bregman算法在遥感图像边缘检测中的应用研究

doi:10.11896/j.issn.1002-137X.2017.12.056

摘要/Abstract

摘要： 针对基于水平集的边缘检测方法抗噪性能差,处理模糊边界和灰度不均匀性的能力弱,计算效率低,边缘检测结果的精确性极大程度上依赖于初始轮廓的位置且曲线演化易陷入极小值等问题,提出一种基于全局最优凸函数变分模型和Split Bregman数字最小化的边缘检测方法。该方法首先根据CV模型的构造原理,并利用Chan的全局最优化思想,构造了一个通用的可以获得全局最优解的凸函数变分模型；在曲线演化和数字最小化迭代过程中,引入了基于Split Bregman的快速迭代算法,可以使曲线在克服噪声等问题的影响下快速、准确、稳定地演化到目标的边界处。实验结果证明了提出的边缘检测方法既具有较高的计算效率,能够满足遥感图像边缘检测对实时性的要求,同时也具有较高的边缘检测精度和良好的普适性。

关键词: 边缘检测,主动轮廓模型,遥感图像,Split Bregman算法

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

景雨,刘建鑫,刘朝霞,李绍华. Split Bregman算法在遥感图像边缘检测中的应用研究[J]. 计算机科学, 2017, 44(12): 310-315. https://doi.org/10.11896/j.issn.1002-137X.2017.12.056

JING Yu, LIU Jian-xin, LIU Zhao-xia and LI Shao-hua. Application Research on Split Bregman Algorithm in Edge Detection of Remote Sensing Image[J]. Computer Science, 2017, 44(12): 310-315. https://doi.org/10.11896/j.issn.1002-137X.2017.12.056

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