电子学报 ›› 2015, Vol. 43 ›› Issue (7): 1375-1381.DOI: 10.3969/j.issn.0372-2112.2015.07.019

• 学术论文 • 上一篇    下一篇

基于小波融合和PCA-核模糊聚类的遥感图像变化检测

慕彩红1, 霍利利1, 刘逸2, 刘若辰1, 焦李成1   

  1. 1. 西安电子科技大学智能感知与图像理解教育部重点实验室, 智能感知与计算国际联合研究中心, 陕西西安 710071;
    2. 西安电子科技大学电子工程学院, 陕西西安 710071
  • 收稿日期:2013-01-15 修回日期:2014-06-18 出版日期:2015-07-25
    • 作者简介:
    • 慕彩红 女,1978年10月出生于河南武陟.2010年6月在西安电子科技大学获博士学位.现为西安电子科技大学电子工程学院副教授、硕士生导师.主要研究方向包括图像处理、模式识别、计算智能等. E-mail:mucaihongxd@foxmail.com;霍利利 女,1987年10月出生于河北邢台.西安电子科技大学电子与通信工程专业硕士研究生.主要研究方向包括图像处理、遥感图像变化检测等. E-mail:lily.home.hao@163.com
    • 基金资助:
    • 国家重点基础研究发展计划 (No.2013CB329402); 国家自然科学基金 (No.61003199,No.61373111,No.61272279,No.61303032,No.61371201); 中央高校基本科研业务费专项资金 (No.JB140216,No.K5051202019,No.K5051302084); 陕西省自然科学基础研究计划 (No.2014JQ5183,No.2014JM8321); 高等学校学科创新引智计划 (No.B07048); 教育部长江学者和创新团队发展计划 (No.IRT1170)

Change Detection for Remote Sensing Images Based on Wavelet Fusion and PCA-Kernel Fuzzy Clustering

MU Cai-hong1, HUO Li-li1, LIU Yi2, LIU Ruo-chen1, JIAO Li-cheng1   

  1. 1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi 710071, China;
    2. School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, China
  • Received:2013-01-15 Revised:2014-06-18 Online:2015-07-25 Published:2015-07-25
    • Supported by:
    • National Key Basic Research Development Plan (No.2013CB329402); National Natural Science Foundation of China (No.61003199, No.61373111, No.61272279, No.61303032, No.61371201); Fundamental Research Funds for the Central Universities (No.JB140216, No.K5051202019, No.K5051302084); Natural Science Basic Research Program of Shaanxi Province (No.2014JQ5183, No.2014JM8321); Overseas Expertise Introduction Project for Discipline Innovation  (“111Project”) (No.B07048); Program for Changjiang Scholars and Innovative Research Team in University (No.IRT1170)

摘要:

本文提出了一种变化检测方法以提高算法的鲁棒性、检测精度以及抗噪性.首先对差值法构造的差异图和比值法构造的差异图进行小波融合.然后将融合图像分成互不重叠的小块,并用主成分分析得到图像块的正交基.通过将融合图像中每个像素的邻域小块映射到正交基上使得每个像素用一个特征向量来表示.最后用基于核的模糊C均值对特征向量进行聚类.实验结果显示与使用单一类型差异图的聚类方法相比,本方法由于采用了图像融合的策略而增强了鲁棒性,且由于采用了核模糊聚类,进一步提高了变化检测精度.此外由于使用了特征提取的技术,本方法具有一定的抗噪性能.

关键词: 遥感图像, 变化检测, 特征提取, 核模糊聚类, 小波融合

Abstract:

A change detection method is proposed to improve the robustness,detection accuracy and noise immunity.Wavelet fusion is employed to combine the difference image obtained by subtraction operator with that obtained by ratio operator.Then,the fused image is partitioned into non-overlapping blocks,and an orthonormal basis is extracted from them through principal component analysis (PCA).Each pixel in the fused image is represented by a feature vector which is the projection of neighborhood patch onto the orthonormal basis.Finally,the change detection image is achieved by clustering the feature vectors using kernel based fuzzy C means (kernel-FCM) clustering algorithm.Experiments show that the strategy of image fusion enhances the robustness of the algorithm when compared with those based on single difference image,and kernel-FCM improves the accuracy further.In addition,due to the use of feature extraction technique,the method performs well on combating noise.

Key words: remote sensing image, change detection, feature extraction, kernel fuzzy clustering, wavelet fusion

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