摘要
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。
In order to realize automatic registration of a Synthetic Aperture Radar（SAR） image,an approach of image multi-scale registration based on affine invariant Fast Kernel Independent Component Analysis-Scale Invariant Feature Transform（FKICA-SIFT） features is presented.First,the affine invariant SIFT descriptors are constructed according to the Hessian matrix of feature points. The FKICA is used to extract the independent components of the affine invariant SIFT descriptors to obtain new descriptors（FKICA-SIFT）.After filtering the input images by using Steerable pyramid,the new descriptors are used to match the feature points detected from the synthetic images of the band-pass sub-images in each layer.Finally,a coarse-to-fine procedure is adopted for gradual optimizing transformation parameters to achieve the multi-scale registration results.Experimental results show that the correct matching rate of proposed algorithm is more than 85% when the threshold is less than 0.7 and that is more than 90% when the threshold is less than 0.5.The registration accuracy of the proposed algorithm can achieve sub-pixel level and it is better than those of SIFT,PCA-SIFT,ICA-SIFT and SURF methods.It has been applied to accurate detection of the changes of the Tangjiashan waters before and after the Wenchuan earthquake,and obtained results meet the requirements of accurate registration for SAR images.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2011年第9期2186-2196,共11页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.60972150No.109261971)
关键词
图像配准
合成孔径雷达图像
尺度不变特征变换
快速核独立成分分析
image registration
Synthetic Aperture Radar（SAR） image
Scale Invariant Feature Transform（SIFT）
Fast Kernel Independent Component Analysis（FKICA）
