Synthetic aperture radar image change detection based on improved bilateral filtering and fuzzy C mean

Abstract

This paper presents an unsupervised synthetic aperture radar (SAR) image change detection method based on improved bilateral filtering and fuzzy C means (FCM). Many previous approaches to change detection are based on a difference image. Unlike conventional approaches, based on difference images, our method demonstrates superior ability to reduce speckle noise and suppress background information, while still retaining edge information effectively. First, the two images are preprocessed using a Lee filter to remove some of the speckle noise. Second, we use the neighbor-log ratio and the Gauss-log ratio to produce initial change maps. Third, we use the improved bilateral to fuse the two change maps, to obtain an initial difference image. Next, we apply a median filter on the initial difference image, to obtain the final difference image. The above method makes full use of the field information, and it can effectively remove speckle noise while still preserving edge information. Finally, an improved FCM algorithm is used to cluster the denoised difference image. Denoising prior to clustering overcomes the main deficiency of conventional clustering algorithms, which is that they are noise sensitive. Empirical experiments, on three groups of SAR images, suggest that the proposed algorithm outperforms several other methods from the literature, in terms of noise suppression, accuracy, and lower change detection error rates.