Coarse-to-fine SAR image change detection method

Abstract

ABSTRACTIn this paper, in order to obtain high-quality training samples and improve the accuracy of the synthetic aperture radar (SAR) image change detection, we propose a coarse-to-fine SAR image change detection method. In the coarse change detection stage, we construct the difference image (DI) by the use of the wavelet frequency difference (WFD) method, and obtain the DI saliency map by the use of quaternion Fourier transform (QFT). At the same time, the noise in the non-salient regions is suppressed. Finally, the coarse change detection map obtained by selecting a threshold for the DI saliency map is pre-classified (changed pixels, unchanged pixels, undetermined pixels) by the fuzzy c-means (FCM) clustering algorithm. In the fine change detection phase, the neighbourhood features of the changed pixels and the unchanged pixels in the coarse change map are extracted and used as reliable samples for extreme learning machine (ELM) training. The trained ELM classifier is then used to perform change detection on the coarse change detection map, to obtain the final change detection map. Experiments on two real SAR datasets show that the proposed method can not only obtain reliable training samples, but it can also result in a significant improvement in change detection performance.