Xiang Yuming, Wang Feng, You Hongjian
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2018 Feb 24;18(2):672. doi: 10.3390/s18020672.
The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR) satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes. In order to further utilize GF-3 SAR images, an automatic and fast image registration procedure needs to be done. In this paper, we propose a novel image registration technique for GF-3 images of different imaging modes. The proposed algorithm consists of two stages: coarse registration and fine registration. In the first stage, we combine an adaptive sampling method with the SAR-SIFT algorithm to efficiently eliminate obvious translation, rotation and scale differences between the reference and sensed images. In the second stage, uniformly-distributed control points are extracted, then the fast normalized cross-correlation of an improved phase congruency model is utilized as a new similarity metric to match the reference image and the coarse-registered image in a local search region. Moreover, a selection strategy is used to remove outliers. Experimental results on several GF-3 SAR images of different imaging modes show that the proposed algorithm gives a robust, efficient and precise registration performance, compared with other state-of-the-art algorithms for SAR image registration.
2016年8月发射的中国高分三号卫星是一颗合成孔径雷达(SAR)卫星,其拥有的成像模式数量位居世界第一。它可在聚束、条带、扫描SAR、海浪、全球观测等成像模式间实现自由切换。为了进一步利用高分三号SAR图像,需要进行自动快速的图像配准。本文提出了一种针对高分三号不同成像模式图像的新型图像配准技术。所提算法包括两个阶段:粗配准和精配准。在第一阶段,我们将自适应采样方法与SAR-SIFT算法相结合,以有效消除参考图像和感测图像之间明显的平移、旋转和尺度差异。在第二阶段,提取均匀分布的控制点,然后将改进的相位一致性模型的快速归一化互相关用作新的相似性度量,在局部搜索区域内对参考图像和粗配准图像进行匹配。此外,还采用了一种选择策略来去除异常值。对多幅不同成像模式的高分三号SAR图像进行实验的结果表明,与其他用于SAR图像配准的先进算法相比,所提算法具有稳健、高效和精确的配准性能。