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Improved method for SAR image registration based on scale invariant feature transform

机译:基于尺度不变特征变换的SAR图像配准改进方法

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摘要

Scale invariant feature transform (SIFT) is one of the most common registration algorithms for synthetic aperture radar (SAR) images. However, the occurrence of speckle noise and geometric distortion within SAR images usually leads to limited effectiveness, challenging the stability of SIFT and its variants in real actual applications. In this study, significant improvements for SAR image registration with SIFT are made, which lie mainly in two aspects. First, a scheme is developed to enhance the description of keypoints with improved dominant orientation assignment and support region. Second, an optimised matching method for further enhancing the matching performance is developed to reduce the mutual interference among the keypoints with similar location and dominant orientations. Extensive experiments confirm the effectiveness of the proposed algorithm for SAR images.
机译:尺度不变特征变换(SIFT)是合成孔径雷达(SAR)图像最常用的配准算法之一。但是,SAR图像中斑点噪声和几何失真的发生通常导致效果有限,从而在实际应用中挑战了SIFT及其变体的稳定性。在这项研究中,通过SIFT对SAR图像配准进行了重大改进,这主要体现在两个方面。首先,开发了一种方案,以通过改进的主导方向分配和支持区域来增强关键点的描述。其次,开发了一种用于进一步增强匹配性能的优化匹配方法,以减少具有相似位置和主导方向的关键点之间的相互干扰。大量实验证实了所提算法对SAR图像的有效性。

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