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.
Image registration is concerned with the precise overlap of two images. One challenging problem in this area is the registration of low-resolution synthetic aperture radar (SAR) images. In general, extracting feature points from such images is difficult due to the coarse observation and the severe speckle. The use of area similarity for image registration is another important branch to solve the problem. A similarity measure based on a conditional density function (cdf) is proposed. The cdf is specially tailored for SAR images, where the speckle is generally assumed as multiplicative gamma noise with unit mean. Additionally, a two-step procedure is devised for the registration of intro-model SAR images to improve the computational efficiency. First, the two images are roughly aligned considering only the translational difference. Then small blocks from the two images are accurately aligned and the center point of each block is treated as a control point, which is finally used to obtain the precise affine transformation between the two images. Five SAR image datasets are tested in the experiment part, and the results demonstrate the efficiency and accuracy 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 registration, Synthetic aperture radar, Feature extraction, Image fusion, Cardiovascular magnetic resonance imaging, Canonical correlation analysis, Image restoration, Image processing, Simulation of CCA and DLA aggregates, Image analysis
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.
Multiresolution-based image fusion has been the focus of considerable research attention in recent years with a number
of algorithms proposed. In most of the algorithms, however, the parameter configuration is usually based on experience.
This paper proposes an adaptive image fusion algorithm based on the nonsubsampled contourlet transform (NSCT),
which realizes automatic parameter adjustment and gets rid of the adverse effect caused by artificial factors. The
algorithm incorporates the quality metric of structural similarity (SSIM) into the NSCT fusion framework. The SSIM
value is calculated to assess the fused image quality, and then it is fed back to the fusion algorithm to achieve a better
fusion by directing parameters (level of decomposition and flag of decomposition direction) adjustment. Based on the
cross entropy, the local cross entropy (LCE) is constructed and used to determine an optimal choice of information
source for the fused coefficients at each scale and direction. Experimental results show that the proposed method
achieves the best fusion compared to three other methods judged on both the objective metrics and visual inspection and
exhibits robust against varying noises.