Unsupervised SAR Image Segmentation Based on Quantum-Inspired Evolutionary Gaussian Mizture Model

摘要：

In this paper,an unsupervised SAR image segmentation algorithm (QEAGMM) based on quantum-inspired evolutionary Gaussian Mixture Models (GMM) is proposed. The method first divides the original image into small blocks. Then,the heterogeneous and homogeneous blocks are obtained using FCM clustering.Finally,the parameters of gaussian mixture model are trained by expectation-maximization (EM) algorithm using a part of homogeneous samples.However,the EM algorithm is apt to fall into a local optimum and the result is sensitive to initialization.So we embed the EM algorithm in quantum evolutionary algorithm (QEA) and propose a quantum-inspired-based EM algorithm (QEA-EM) to train the gaussian mixture mouel. This method not only improves the accuracy of parameters estimation but also performs better than immune clonal selection EM algorithm (ICSEM) on computational complexity. The experimental results show that compared to gaussian mixture model clustering algorithm (GMMC),the proposed method is successfully applied to texture mosaic images and SAR images,and shows overall improvement in performance.

关键词： Quantum-inspired evolutionary algorithm ezpectation-mazimization algorithm Gaussian mizture models SAR images

作者: Fang Liu Yingying Liu Hongxia Hao

作者单位: The School of Computer Science and Technology,Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China,Xidian University,Xian 710071,China

会议类型: 国际会议

会议名称: 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar(第二届亚太合成孔径雷达会议)

会议地点: 西安

会议语种:英文

页码: 809-812

在线出版日期: 2009-10-26（万方平台首次上网日期，不代表论文的发表时间）