Using Combined Difference Image and $k$ -Means Clustering for SAR Image Change Detection

Abstract

In this letter, a simple and effective unsupervised approach based on the combined difference image and k-means clustering is proposed for the synthetic aperture radar (SAR) image change detection task. First, we use one of the most popular denoising methods, the probabilistic-patch-based algorithm, for speckle noise reduction of the two multitemporal SAR images, and the subtraction operator and the log ratio operator are applied to generate two kinds of simple change maps. Then, the mean filter and the median filter are used to the two change maps, respectively, where the mean filter focuses on making the change map smooth and the local area consistent, and the median filter is used to preserve the edge information. Second, a simple combination framework which uses the maps obtained by the mean filter and the median filter is proposed to generate a better change map. Finally, the k-means clustering algorithm with k = 2 is used to cluster it into two classes, changed area and unchanged area. Local consistency and edge information of the difference image are considered in this method. Experimental results obtained on four real SAR image data sets confirm the effectiveness of the proposed approach.