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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >AN IMPROVED IMAGE MATCHING METHOD BASED ON SURF ALGORITHM
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AN IMPROVED IMAGE MATCHING METHOD BASED ON SURF ALGORITHM

机译：基于surf算法的改进图像匹配方法

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Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.
机译：基于特征匹配的许多最新的图像匹配方法已经在遥感领域得到了广泛的研究。这些具有高操作效率的特征匹配方法具有精度和鲁棒性低的缺点。提出了一种基于SURF算法的改进图像匹配方法。该方法引入了颜色不变变换，信息熵理论和一系列约束条件，以提高特征点的检测和匹配精度。首先，针对两个匹配图像引入了颜色不变变换模型，旨在在匹配过程中获得更多的颜色信息，并利用信息熵理论获得了两个匹配图像的最多信息。然后应用SURF算法从图像中检测和描述点。最后，采用包括Delaunay三角剖分构造，相似性函数和射影不变量的约束条件来消除不匹配，从而提高匹配精度。该方法已在遥感影像上得到验证，其精度高，鲁棒性强。

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