Optical and SAR image registration based on improved nonsubsampled wavelet transform for sea islands

SHI Wei; SU Fenzhen; WANG Ruirui; LU Yongduo

doi:10.1007/s13131-014-0474-x

海岛礁光学和雷达影像基于改进非子采样小波变换的自动配准

doi: 10.1007/s13131-014-0474-x

1.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China
2.
Beijing Forestry University, Beijing 100083, China
3.
National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China

基金项目: The National Natural Science Foundation of China under contract No. 41271409; the National Key Technology Research and Development Program under contract No. 2011BAH23B00; the National High Technology Research and Development Program (863 Program) of China under contract No. 2012AA12A406.

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出版历程
- 收稿日期: 2012-12-05
- 修回日期: 2013-09-22

Optical and SAR image registration based on improved nonsubsampled wavelet transform for sea islands

1.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China
2.
Beijing Forestry University, Beijing 100083, China
3.
National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China

摘要

摘要: 岛礁光学和雷达影像中同名特征点较少，使得同名特征点的提取成为两者之间自动配准的关键难题。针对这个问题，文中采用一种阈值收缩算子对非子采样小波变换进行改进，研究了一种基于改进的非子采样小波变换的配准算法。另外，与传统的匹配方法不同，文中采用的基于低频波段圆形邻域的匹配策略能够提取到大量高可靠性的同名特征点对，保证了岛礁光学和雷达遥感影像的高精度配准。文中选取尺度偏差显著的SPOT-5(MS)和遥感一号的雷达影像组合进行试验，结果证明以上算法能够检测到大量分布均匀的同名特征点对。文中分别基于改进的非子采样小波变换和传统的小波变换计算得到两个配准模型，对两个配准模型的均方根误差分布进行了比较分析，进一步证明基于改进的非子采样小波变换配准算法的性能。该研究可为中国南海岛礁遥感数据的融合和目标识别提供前提条件。
- 南海岛礁影像自动配准,非子采样小波变换(NSWT),阈值收缩算子
Abstract: Homologous feature point extraction is a key problem in the optical and synthetic aperture radar (SAR) image registration for islands. A new feature point extraction method using a threshold shrink operator and non-subsampled wavelet transform (TSO-NSWT) for optical and SAR image registration was proposed. Moreover, the matching for this proposed feature was different from the traditional feature matching strategies and was performed using a similarity measure computed from neighborhood circles in low-frequency bands. Then, a number of reliably matched couples with even distributions were obtained, which assured the accuracy of the registration. Application of the proposed algorithm to SPOT-5 (multi-spectral) and YG-1 (SAR) images showed that a large number of accurately matched couples could be identified. Additionally, both of the root mean square error (RMSE) patterns of the registration parameters computed based on the TSO-NSWT algorithm and traditional NSWT algorithm were analyzed and compared, which further demonstrated the effectiveness of the proposed algorithm. The algorithm can supply the crucial step for island image registration and island recognition.
- image registration

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参考文献(18)

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海岛礁光学和雷达影像基于改进非子采样小波变换的自动配准

doi: 10.1007/s13131-014-0474-x

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出版历程

Optical and SAR image registration based on improved nonsubsampled wavelet transform for sea islands

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