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.