Multitemporal Images Change Detection Using Nonsubsampled Contourlet Transform and Kernel Fuzzy C-Means Clustering

Abstract

In this paper, an unsupervised change detection method for multitemporal remote sensing images is proposed. Firstly, the difference image is obtained from two multitemporal images acquired on the same geographical area but at different time instances. Then the difference image is decomposed by nonsubsampled contour let transform (NSCT). For each pixel in the difference image, a feature vector is extracted using the NSCT coefficients and the difference image itself which are in the same position. The final change map is achieved by clustering the feature vectors using kernel fuzzy c-means (KFCM) clustering algorithm into two classes: changed and unchanged. The change detection results are compared with those of several state-of-the-art methods. And the experimental results demonstrate that the proposed method yields superior performance.