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纸质出版日期： 2018-3 ，
录用日期： 2017-9-18
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何梦梦, 郭擎, 李安, 陈俊, 陈勃, 冯旭祥. 2018. 特征级高分辨率遥感图像快速自动配准. 遥感学报, 22(2): 277–292
He M M, Guo Q, Li A, Chen J, Chen B and Feng X X. 2018. Automatic fast feature-level image registration for high-resolution remote sensing images. Journal of Remote Sensing, 22(2): 277–292
随着遥感图像分辨率的日益提高，遥感图像的尺寸和数据量也不断地增大，同时随着遥感应用的发展，对图像配准的性能也提出越来越高的要求，基于此，提出一种特征级高分辨率遥感图像快速自动配准方法。首先，对图像进行Haar小波变换，基于小波变换后的近似图像进行配准以提高配准速度；其次，根据不同的遥感图像来源使用不同的特征提取方法(光学图像使用Canny边缘提取算子，SAR图像使用Ratio Of Averages算子)，并将线特征转化为点特征；然后，依据特征点间最小角与次小角的角度之比小于某一阈值来确定初始匹配点对；最后，利用改进的随机抽样一致性算法滤除错误匹配点对，并结合分块思想均匀选取匹配点对计算仿射变换参数，进一步提高配准精度。为了验证本文方法的有效性，选择高分辨率WorldView-2图像、Pleiades图像和TerraSAR图像进行了实验，并与典型的SIFT算法、SURF算法进行比较分析，采用匹配率、匹配效率、均方根误差和时间消耗4个定量评价指标来客观评价算法的配准性能。实验结果表明，本文方法具有较好的有效性，且在不同的情况下具有较高的配准精度。本文提出的特征级高分辨率遥感图像快速自动配准方法，多组高分辨率遥感图像数据的配准实验结果表明该方法能快速实现并具有较高的配准精度和较好的鲁棒性。
The size and amount of remote sensing images constantly increase with the improving resolution of remote sensing images. Meanwhile
the development of remote sensing applications also requires high image registration performance. Therefore
an automatic fast feature-level image registration method for high-resolution remote sensing images is proposed. The method includes five steps. First
the reference image and the image to be registered are processed by Haar wavelet transform to obtain the low-frequency approximate images to match. Then
the original images are registered according to the matching result of the approximate images
thereby potentially effectively reducing calculation and improving registration speed. Second
edges in the optical image are extracted by the Canny operator
and edges in the SAR image are extracted by the Ratio Of Averages (ROA) operator. Then
the edge line features are transformed into point features. The use of edge point features can achieve positioning accuracy and acquire stable features. Third
in the feature matching session
the main and auxiliary directions of the point features are considered such that each point feature has multiple directions to enhance the robustness of image registration. Then
the initial matching points are determined by the ratio of the minimum angle to the second minimum angle
which is less than a threshold. Fourth
in the matching point pair filtering session
the random sample consensus is enhanced to improve registration accuracy by adding the constraint condition. The high-quality matching point pairs are selected to fit the model parameters. Finally
in the affine session
the block thought is used to uniformly select matching point pairs to be evenly distributed in the image to avoid the local optimal problem on the registration and further improve image registration accuracy. To verify the efficiency of the method
experiments are conducted under the following conditions: the same sensor optical image registration and sensor SAR image registration
image registration among different bands
image registration with different resolutions
and image registration of different satellite sensors with large size. High resolution WorldView-2
Pleiades
and TerraSAR images are used to perform the experiments. The proposed method is compared with the typical SIFT and SURF algorithms. Four quantitative evaluation indexes
namely
Matching Ratio (MR)
Matching Efficiency (ME)
Root Mean Square Error (RMSE)
and time consumed are used for the registration result evaluation. Experimental results show that the proposed method effectively achieves high registration accuracy under the different conditions. An automatic fast feature-level image registration method for high-resolution remote sensing images is proposed. Multiple datasets of registration experimental results for high-resolution remote sensing images indicate that the proposed method can be rapidly implemented and has high accuracy and strong robustness.
遥感图像配准高分辨率遥感图像自动配准图像匹配特征匹配特征提取
remote sensing registrationhigh resolution remote sensing image automatic registrationimage matchingfeature matchingfeature extraction
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