Image matching is one of the key technologies of UAV video surveillance. In order to improve the accuracy and efficiency of image matching with different resolutions and scales, a UAV image fast matching method is proposed. Based on the fast SIFT algorithm, the significant areas of the entire image are extracted from the whole image through histogram calculation, and the feature points are calculated on the significant areas. The feature points in two frames are matched, and the mismatched points are eliminated by parallel line algorithm to improve the SIFT feature matching efficiency. The experimental results show that the proposed method not only reduces the matching time, but also improves the accuracy of image matching while maintaining the image matching rate and the robustness of the algorithm, which proves the effectiveness of image matching.
Remote sensing technology plays a crucial role in today's world, providing a large amount of data for various fields. Different fields require detailed analysis of different types of product characteristics to meet different needs. Therefore, in-depth understanding of product characteristics is crucial to improve the efficiency of decision making and target management. The existing data comparative analysis methods of product characteristics often only carry out a single dimension comparison, which is not conducive to users to understand the target product characteristics. This paper designs a comparative analysis method of multi-dimensional data based on product characteristics. By obtaining different types of raw data of the target, feature extraction is carried out on each type of raw data respectively to obtain the corresponding feature data of each type, and different feature data are normalized. The normalized feature data is compared and displayed with the current type of known feature data of each known target in the database, and the comparison results of each dimension of the target are obtained.
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