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

MU Cai-hong; HUO Li-li; LIU Yi; LIU Ruo-chen; JIAO Li-cheng

doi:10.3969/j.issn.0372-2112.2015.07.019

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

更新时间：2025-07-16

- Change Detection for Remote Sensing Images Based on Wavelet Fusion and PCA-Kernel Fuzzy Clustering
- Acta Electronica Sinica Vol. 43, Issue 7, Pages: 1375-1381(2015)
- 作者机构：
  
  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
  3. 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
  4. 西安电子科技大学电子工程学院,陕西,西安,710071
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.07.019
  CLC： TP751.1
- Published：2015
- 稿件说明：
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MU Cai-hong, HUO Li-li, LIU Yi, et al. Change Detection for Remote Sensing Images Based on Wavelet Fusion and PCA-Kernel Fuzzy Clustering[J]. Acta Electronica Sinica, 2015, 43(7): 1375-1381.
DOI：

MU Cai-hong, HUO Li-li, LIU Yi, et al. Change Detection for Remote Sensing Images Based on Wavelet Fusion and PCA-Kernel Fuzzy Clustering[J]. Acta Electronica Sinica, 2015, 43(7): 1375-1381. DOI： 10.3969/j.issn.0372-2112.2015.07.019.

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.

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.

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