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首页> 外文会议>International Conference on Computer Vision, Image and Deep Learning >Remote sensing image registration based on fusion of spatial transformation and dense convolution
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Remote sensing image registration based on fusion of spatial transformation and dense convolution

机译：基于空间变换和密集卷积的遥感图像配准

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摘要

In order to achieve high-precision registration of remote sensing images, this paper proposes a remote sensing image registration method based on spatial transformation and dense convolution fusion. First, the dense convolutional network improved by the spatial transformation module is used for feature extraction to improve the robustness of feature points; then use improved Grid-based Motion Statistics (GMS) algorithm for feature matching, and use the homography matrix-based method to eliminate mismatched point pairs to improve matching accuracy; finally, the transformed model is used to solve the transformed model to achieve accurate registration of remote sensing images. Experiments show that the proposed method can effectively improve the correct matching rate of feature points, has higher registration accuracy and stronger robustness.
机译：为了实现遥感图像的高精度注册，本文提出了一种基于空间变换和密集卷积融合的遥感图像配准法。首先，通过空间变换模块改善的密集卷积网络用于特征提取，以提高特征点的鲁棒性;然后使用改进的基于网格的运动统计（GMS）算法进行特征匹配，并使用基于同性恋矩阵的方法来消除不匹配的点对以提高匹配精度;最后，转换模型用于解决变换模型以实现遥感图像的准确登记。实验表明，该方法可以有效地提高特征点的正确匹配速率，具有更高的登记精度和更强的鲁棒性。

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