SAR image multi-scale registration based on FKICA-SIFT features

LIU Xiang-zeng; TIAN Zheng; SHI Zhen-guang; CHEN Zhan-shou

doi:10.3788/OPE.20111909.2186

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SAR image multi-scale registration based on FKICA-SIFT features

Article | 更新时间：2020-08-12

- SAR image multi-scale registration based on FKICA-SIFT features
- Optics and Precision Engineering Vol. 19, Issue 9, Pages: 2186-2196(2011)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,130033
  2. 西北工业大学理学院,陕西西安,710129
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111909.2186
  CLC： TP751;TP391.4
- Received：16 December 2010，
  
  Revised：29 January 2011，
  
  Published Online：26 September 2011，
  
  Published：26 September 2011
- 稿件说明：
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刘向增, 田铮, 史振广, 陈占寿. 基于FKICA-SIFT特征的合成孔径图像多尺度配准[J]. 光学精密工程, 2011,19(9): 2186-2196

LIU Xiang-zeng, TIAN Zheng, SHI Zhen-guang, CHEN Zhan-shou. SAR image multi-scale registration based on FKICA-SIFT features[J]. Editorial Office of Optics and Precision Engineering, 2011,19(9): 2186-2196
刘向增, 田铮, 史振广, 陈占寿. 基于FKICA-SIFT特征的合成孔径图像多尺度配准[J]. 光学精密工程, 2011,19(9): 2186-2196 DOI： 10.3788/OPE.20111909.2186.

LIU Xiang-zeng, TIAN Zheng, SHI Zhen-guang, CHEN Zhan-shou. SAR image multi-scale registration based on FKICA-SIFT features[J]. Editorial Office of Optics and Precision Engineering, 2011,19(9): 2186-2196 DOI： 10.3788/OPE.20111909.2186.

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。

Abstract

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.

关键词

Keywords

references

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