基于小波融合和PCA-核模糊聚类的遥感图像变化检测

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.

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

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.