Sar image change detection method based on visual attention

Abstract

Change detection is a hot issue and is of great significance in remote sensing. The logarithm operation is a valid way to reduce the influence of multiplicative noise in the Synthetic Aperture Radar (SAR) image. However, changed areas with high gray level values will be weakened due to the nature of the logarithmic function. In this paper, a SAR image change detection framework based on visual attention is proposed. In the proposed method, the SAR image change detection is finished with an extreme method with darkness and brightness on the vision. The main process can be divided into two parts according to dark and bright image patches. The dark changed areas are validly detected via a weighted logarithmic function, which has strong noise immunity. The weak bright changes are taken as noise. The saliency extraction is applied on the initial SAR image patches to enhance the bright changed areas whereas others present murky background. Then bright changed areas can be validly detected using kernel fuzzy c-means (KFCM), in which the cross-time similarities function between image patches is used. Finally, two change maps can be added to obtain final result. The real SAR image pairs of Suzhou area are used to verify proposed change detection method. The experimental results demonstrate the effectiveness of the proposed method.