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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A New Sample Consensus Based on Sparse Coding for Improved Matching of SIFT Features on Remote Sensing Images
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A New Sample Consensus Based on Sparse Coding for Improved Matching of SIFT Features on Remote Sensing Images

机译：基于稀疏编码的新的示例共识，用于改进遥感图像上的SIFT功能匹配

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摘要

In this article, a new method is proposed for feature matching of remote sensing images using sample consensus based on sparse coding (SCSC) to improve the image registration technique. To this end, scale-invariant feature transform (SIFT) features are used to select interesting points for image matching. The extracted points contain some differences and similarities in two images captured from the same area (but different in sensor resolution, azimuth, elevation, contrast, illumination, etc.); in such a case, similar points should be extracted and other dissimilar should be eliminated. In this article, we greatly improve the matching between two images using the SCSC through checking points altogether. Moreover, the proposed method is shown to have better results than standard alternative methods such as random sample consensus (RANSAC) when the number of feature points is too much or have noise. However, it should be noted that for a low-noise and distortion rate, the proposed method and the RANSAC yield similar results. In general, the proposed method using sparse coding achieves a higher correct match rate than the SIFT algorithm. In order to illustrate this issue, the proposed method is compared to other updated matching and registration methods based on the SIFT algorithm. The obtained results confirm the accuracy of this claim and show that the proposed algorithm is accurate between 0.48% and 7.68% rather than SVD-RANSAC, Hoge, Stone, Foroosh, Leprince, Nagashima, Guizar, Youkyung, Lowe, Preregistration, IS-SIFT, SPSA, Gong, Standard SIFT, IS-SIFT, UR-SIFT, Sourabh, and Han methods.
机译：在本文中，提出了一种新方法，用于基于稀疏编码（SCSC）使用样本共识的遥感图像的特征匹配，以改善图像配准技术。为此，使用Scale-Invariant功能转换（SIFT）功能来选择用于图像匹配的有趣点。提取的点包含在来自同一区域捕获的两个图像中的一些差异和相似度（但在传感器分辨率，方位角，高程，对比度，照明等中的不同）;在这种情况下，应该提取类似的点，并且应该消除其他不同的异常。在本文中，我们通过完全检查点，大大改善了两个图像之间的匹配。此外，当特征点的数量过多或具有噪声时，所提出的方法被示出比标准替代方法（如随机样本共识（RANSAC））具有更好的结果。然而，应该注意的是，对于低噪声和失真率，所提出的方法和RANSAC产生类似的结果。通常，使用稀疏编码的所提出的方法实现比SIFT算法更高的正确匹配速率。为了说明这个问题，将所提出的方法与基于SIFT算法的其他更新的匹配和登记方法进行比较。所获得的结果证实了本发明要求的准确性，并表明该算法准确到0.48％和7.68％而不是SVD-Ransac，Hoge，Stone，Foroosh，Leprince，Nagashima，Guizar，Youkyung，Lowe，预更，是筛选的，SPSA，龚，标准筛，是筛选，UR-SIFT，SORKABH和HAN方法。

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