In this paper, a novel approach to change detection in synthetic aperture radar (SAR) images based on structure similarity (SSIM) and parametric kernel graph cuts is presented. Firstly, the SSIM is imported into change detection and a difference image constructed method based on SSIM is proposed. And then, the changed and unchanged pixels are identified from the difference image by the parametric kernel graph cuts algorithm. Experimental results obtained on real SAR images demonstrate the effectiveness of the proposed method.
Proc. SPIE. 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
KEYWORDS: Image fusion, Synthetic aperture radar, Image processing, Image restoration, Feature extraction, Image analysis, Image registration, Cardiovascular magnetic resonance imaging, Canonical correlation analysis, Simulation of CCA and DLA aggregates
Referring to the problem of SAR image registration, an image registration method based on Scale Invariant Feature Transform (SIFT) and Multi-Scale Autoconvolution (MSA) is proposed. Based on the extraction of SIFT descriptors and the MSA affine invariant moments of the region around the keypoints, the feature fusion method based on canonical correlation analysis (CCA) is employed to fuse them together to be a new descriptor. After the control points are rough matched, the distance and gray correlation around the rough matched points are combined to build the similarity matrix and the singular value decomposition (SVD) method is employed to realize precise image registration. Finally, the affine transformation parameters are obtained and the images are registered. Experimental results show that the proposed method outperforms the SIFT method and achieves high accuracy in sub-pixel level.