针对合成孔径(SAR)图像的配准，提出一种基于仿射不变快速核独立成分分析-尺度不变特征变换(FKICA-SIFT)的多尺度配准方法。首先，根据特征点的Hessian矩阵构建仿射不变SIFT描述子。接着，利用FKICA提取该描述子的独立成分得到新的描述子FKICA-SIFT。然后，利用该描述子对Steerable滤波后的各层带通合成子图像提取的特征点进行匹配。最后，采用由粗到细的匹配策略逐步优化变换参数，实现图像的多尺度精确配准。实验结果表明，对有较大仿射变化的SAR图像，当阈值小于0.7时，该方法的匹配正确率大于85%，阈值小于0.5时，匹配正确率可达90%以上，配准精度达到亚像素水平, 优于SIFT，PCA-SIFT，ICA-SIFT及SURF等相关方法。使用该方法准确地检测出了地震前后唐家山堰塞湖水域的变化情况，基本满足了SAR图像变换检测前精确配准的要求。

A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed. Affine invariant SIFT descriptor is first developed in an elliptical region determined by the Hessian matrix of the feature points. Thereafter, the descriptor is mapped to a feature space induced by a kernel, and a new descriptor is constructed by whitening the mapped descriptor in the feature space, with the transform called KW-SIFT. In a final step, the new descriptor is used to register remote-sensing images. Experimental results for remote-sensing image registration indicate that the proposed method improves the registration performance as compared with other related methods.

针对SAR (Synthetic Aperture Radar) 图像全自动配准问题，本文提出一种基于仿射不变SIFT (Scale InvariantFeature Transform) 特征的精确配准方法。该方法首先对传统SIFT 方法改进构建具有仿射不变性的SIFT 描述子，并利用该描述子对提取的控制点进行粗匹配，然后由粗匹配点对的尺度比和方位差及其邻域的灰度相似性构建新的相似矩阵，最后利用SVD (Singular Value Decomposition)方法确定精确匹配点对，求出变换参数从而实现图像的精确配准。实验结果表明该方法优于传统的SIFT 方法和SIFT＋SVD 方法并且可以达到亚像素的配准精度。