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SAR Image Registration Using Multiscale Image Patch Features With Sparse Representation

机译:使用具有稀疏表示的多尺度图像补丁功能进行SAR图像配准

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摘要

In this paper, we propose a new image registration method for synthetic aperture radar (SAR) image with multiscale image patch features, in which the sparse representation technique is exploited. Considering the influence of speckle noise on feature extraction, in the proposed method, a spatial correlation strategy based on stationary wavelet transform is adopted to select the reliable feature points from the initial scale invariant feature transform keypoints in the reference image. By introducing multiscale image patch, a new feature descriptor is further designed to describe the attribute domain of feature points for higher discrimination. The corresponding points in the sensed image are established based on the minimum discrepancy criterion calculated by the sparse representation technique. Moreover, the local geometric consistency among a feature point and its nearest neighbor points is employed to remove the mismatches from the tentative matches. Twenty-two pairs of SAR images acquired under various conditions are utilized to validate the effectiveness of the proposed method. Compared with the traditional SAR image registration methods, the results show that the proposed method is competent to improve the registration performance substantially.
机译:本文提出了一种具有稀疏表示技术的具有多尺度图像斑块特征的合成孔径雷达图像配准方法。考虑到斑点噪声对特征提取的影响,提出了一种基于平稳小波变换的空间相关策略,从参考图像的初始尺度不变特征变换关键点中选择可靠的特征点。通过引入多尺度图像补丁,进一步设计了新的特征描述符来描述特征点的属性域,以实现更高的辨别力。基于由稀疏表示技术计算出的最小差异标准来建立感测图像中的对应点。此外,特征点及其最近的邻居点之间的局部几何一致性被用来从暂定匹配中去除不匹配。利用22对不同条件下获得的SAR图像来验证所提方法的有效性。结果表明,与传统的SAR图像配准方法相比,该方法能够有效提高配准性能。

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