基于小波融合和PCA-核模糊聚类的遥感图像变化检测

doi:10.3969/j.issn.0372-2112.2015.07.019

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

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. 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. 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

摘要/Abstract

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

中图分类号:

TP751.1

慕彩红, 霍利利, 刘逸, 刘若辰, 焦李成. 基于小波融合和PCA-核模糊聚类的遥感图像变化检测[J]. 电子学报, 2015, 43(7): 1375-1381.

MU Cai-hong, HUO Li-li, LIU Yi, LIU Ruo-chen, JIAO Li-cheng. Change Detection for Remote Sensing Images Based on Wavelet Fusion and PCA-Kernel Fuzzy Clustering[J]. Acta Electronica Sinica, 2015, 43(7): 1375-1381.

参考文献

[1] Gong M,Zhou Z,Ma J.Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering[J].IEEE Transactions on Image Processing,2012,21(4):2141-2151.
[2] 马国锐,李平湘,秦前清.基于融合和广义高斯模型的遥感影像变化检测[J].遥感学报,2006,10(6):847-853. Ma Guo-rui,Li Ping-xiang,Qin Qian-qing.Based on fusion and GGM change detection approach of remote sensing image[J].Journal of Remote Sensing,2006,10(6):847-853.(in Chinese)
[3] 王桂婷,王幼亮,焦李成.自适应空间邻域分析和瑞利-高斯分布的多时相遥感影像变化检测[J].遥感学报,2009,13(4):639-646. Wang Gui-ting,Wang You-liang,Jiao Li-cheng.Adaptive spatial neighborhood analysis and Rayleigh-Gauss distribution fitting for change detection in multi-temporal remote sensing images[J].Journal of Remote Sensing,2009,13(4):639-646.(in Chinese)
[4] Bruzzone L,Prieto D F.Automatic analysis of the difference image for unsupervised change detection[J].IEEE Transactions on Geoscience and Remote Sensing,2000,38(3):117l-l182.
[5] 王桂婷,王幼亮,焦李成.基于快速EM算法和模糊融合的多波段遥感影像变化检测[J].红外和毫米波学报,2010,29(5):383-388. Wang Gui-ting,Wang You-liang,Jiao Li-cheng.Change detection method of multiband remote sensing image based on fast expectation-maximization algorithm and fuzzy fusion[J].Journal of Infrared and Millimeter Waves,2010,29(5):383-388.(in Chinese)
[6] Bazi Y,Bruzzone L,Melgani F.An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):874-887.
[7] Ghosh A,Mishra N S,Ghosh S.Unsupervised change detection of remotely sensed images using fuzzy clustering[A].Proceedings of the Seventh International Conference on Advances in Pattern Recognition[C].USA:IEEE,2009.385-388.
[8] 李阳阳,石洪竺,焦李成,等.基于流形距离的量子进化聚类算法[J].电子学报,2011,39(10):2343-2347. Li Yang-yang,Shi Hong-zhu,Jiao Li-cheng,et al.Quantum-inspired evolutionary clustering algorithm based on manifold distance[J].Acta Electronica Sinica,2011,39 (10):2343-2347.(in Chinese)
[9] 李阳阳,吴娜娜,焦李成,等.基于量子免疫克隆聚类的SAR图像变化检测[J].红外和毫米波学报,2011,30(4):372-376. Li Yang-yang,Wu Na-na,Jiao Li-cheng,et al.Change detection for SAR images based on quantum-inspired immune clonal clustering algorithm[J].Journal of Infrared and Millimeter Waves,2011,30(4):372-376.(in Chinese)
[10] 常宝,张弓.基于几何结构的SAR图像无监督变化检测方法[J].电子学报,2011,39(9):2125-2129. Chang Bao,Zhang Gong.An unsupervised approach based on Geometrical structures to automatic change detection in multitemporal SAR images[J].Acta Electronica Sinica,2011,39 (9):2125-2129.(in Chinese)
[11] Celik T.Unsupervised change detection in satellite images using principal component analysis and k-means clustering[J].IEEE Geoscience and Remote Sensing Letters,2009,6(4):772-776.
[12] Bazi Y,Bruzzone L,Melgani F.An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):874-887.
[13] Dekker R J.Speckle filtering in satellite SAR change detection imagery[J].International Journal of Remote Sensing,1998,19(6):1133-1146.
[14] Zhang D Q,Chen S C.Fuzzy clustering using kernel methods[A].Proceedings of International Conference on Control and Automation[C].Xiamen:IEEE,2002.123-127.
[15] Kim D W,Lee K Y,Lee D,et al.Evaluation of the performance of clustering algorithms in kernel-induced feature space[J].Pattern Recognition,2005,38(4):607-611.
[16] Chen S C,Zhang D Q.Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J].IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics,2004,34(4):1907-1916.
[17] Graves D,Pedrycz W.Kernel-based fuzzy clustering and fuzzy clustering:A comparative experimental study[J].Fuzzy Sets and Systems,2010,161(4):522-543.

基于小波融合和PCA-核模糊聚类的遥感图像变化检测

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	曲立国, 陈国豪, 胡俊, 陈鹏. 单次扫描连通域分析算法研究综述[J]. 电子学报, 2022, 50(6): 1521-1536.
[2]	杨曦, 张鑫, 郭浩远, 王楠楠, 高新波. 基于不变特征的多源遥感图像舰船目标检测算法[J]. 电子学报, 2022, 50(4): 887-899.
[3]	冯西安, 寇思玮, 谭伟杰, 毕杨. 水声信号处理中的稀疏表示理论及应用[J]. 电子学报, 2021, 49(9): 1840-1851.
[4]	林金朝, 李必禄, 李国权, 黄正文, 庞宇. 基于EWT和结构特征提取的心电信号R波识别算法[J]. 电子学报, 2021, 49(6): 1217-1223.
[5]	徐元博, 江祥奎, 李龙. 高阶对称差分解析能量算子在微弱轴承故障信号提取中的应用[J]. 电子学报, 2021, 49(4): 750-759.
[6]	周勇, 陈思霖, 赵佳琦, 张迪, 王瀚正. 基于弱语义注意力的遥感图像可解释目标检测[J]. 电子学报, 2021, 49(4): 679-689.
[7]	汪宇玲, 陈立, 黎明, 钟国韵, 何月顺, 常玉祥, 宋伟宁. 基于迹变换和旋转增量调制特征的模糊人脸识别[J]. 电子学报, 2021, 49(12): 2437-2448.
[8]	鲍中新, 文成林, 马雪. 一种基于数据变化率的预处理及主元分析故障诊断方法[J]. 电子学报, 2021, 49(11): 2234-2240.
[9]	秦福强, 王丽芳. 基于变分的多尺度遥感图像融合算法[J]. 电子学报, 2020, 48(6): 1084-1090.
[10]	黄鸿, 王丽华, 石光耀. 面向高光谱遥感影像分类的监督多流形鉴别嵌入方法[J]. 电子学报, 2020, 48(6): 1099-1107.
[11]	翟颖, 陈渤. 基于稳健变分自编码模型的雷达高分辨距离像目标识别算法[J]. 电子学报, 2020, 48(6): 1149-1155.
[12]	罗会兰, 陈鸿坤. 基于深度学习的目标检测研究综述[J]. 电子学报, 2020, 48(6): 1230-1239.
[13]	张丞, 何坚, 王伟东. 空间上下文与时序特征融合的交警指挥手势识别技术[J]. 电子学报, 2020, 48(5): 966-974.
[14]	郭小英, 李文书, 钱宇华, 白茹意, 贾春花. 可计算图像复杂度评价方法综述[J]. 电子学报, 2020, 48(4): 819-826.
[15]	刘颖, 刘红燕, 范九伦, 公衍超, 李莹华, 王富平, 卢津. 基于深度学习的小目标检测研究与应用综述[J]. 电子学报, 2020, 48(3): 590-601.