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Remote Sensing Image Matching Based Improved ORB in NSCT Domain

机译:基于NSCT域的遥感图像匹配基于改进的ORB

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摘要

Aiming at the problem that the ORB algorithm has no scale invariance and low matching accuracy in image matching, an improved ORB algorithm is proposed on the basis of SURF algorithm. Based on the flexibility of NSCT in image decomposition and the effectiveness of the improved ORB algorithm in remote sensing image matching, an improved ORB algorithm based on NSCT domain is proposed for remote sensing image matching. The image to be matched and the reference image are decomposed by NSCT. Two corresponding low-frequency images are obtained. Then, to reduce the influence of high-frequency noise on matching results, two low-frequency images are inputted to the improved ORB algorithm to obtain initial match results. The RANSAC algorithm is adopted to eliminate the mismatching points and complete the image matching. The experimental results show that the algorithm can make up the problem that the ORB algorithm has no scale invariance, and effectively improve the matching speed and accuracy of scale and rotation changes between two images. Meanwhile, the algorithm is more robust than classical methods in many complex situations such as image blur, field of view change, and noise interference.
机译:针对ORB算法在图像匹配中没有缩放不变性和低匹配精度的问题,基于冲浪算法提出了一种改进的ORB算法。基于图像分解中NSCT的灵活性以及遥感图像匹配中的改进的ORB算法的有效性,提出了一种基于NSCT域的改进的ORB算法,用于遥感图像匹配。要匹配的图像和参考图像由NSCT分解。获得两个相应的低频图像。然后,为了降低高频噪声对匹配结果的影响,将两个低频图像输入到改进的ORB算法以获得初始匹配结果。采用RANSAC算法消除不匹配点并完成图像匹配。实验结果表明,该算法可以弥补ORB算法没有规模不变性的问题,有效地提高了两个图像之间的匹配速度和旋转变化的匹配速度和准确性。同时,该算法比在许多复杂情况下的经典方法更稳健,例如图像模糊,视场改变和噪声干扰。

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