目的 图像匹配是遥感图像镶嵌拼接的重要环节，图像匹配技术通常采用两步法，首先利用高维描述子的最近和次近距离比建立初始匹配，然后通过迭代拟合几何模型消除错误匹配。尽管外点过滤算法大幅提高了时间效率，但其采用传统的两步法，构建初始匹配的方法仍然非常耗时，导致整个遥感图像拼接的速度提升仍然有限。为了提高遥感图像匹配的效率，本文提出了一种基于空间分治思想的快速匹配方法。方法 首先，通过提取图像的大尺度特征生成少量的初始匹配，并基于初始匹配在两幅图像之间构建成对的分治空间中心点；然后，基于范围树搜索分治空间中心点一定范围内的相邻特征点，构造成对分治空间点集；最后，在各个分治空间点集内分别进行遥感图像特征的匹配。结果 通过大量不同图像尺寸和相对旋转的遥感图像的实验表明，与传统的和其他先进方法相比，本文方法在保证较高精度的同时将匹配时间缩短到1/1001/10。结论 利用初始种子匹配构建分治匹配中心以将图像匹配分解在多个子区间进行的方法有助于提高遥感影像匹配的效率，该算法良好的时间性能对实时遥感应用具有实际价值。

Objective Image matching is crucial to remote sensing. A number of feature matching algorithms have been designed to establish correspondences and filter outliers. The current remote sensing image matching methods have derived from region-based and feature based analyses. Feature-based methods illustrate greater robustness and accuracy in processing complex scenarios like brightness changes, homogeneous textures, and geometric distortions. Feature-based methods are implemented in at two stages as follows: First, robust feature points are extracted and nearest-neighbor distance ratios (NNDRs) is facilitated to clarify putative matches. Next, a geometric model or spatial structure is adopted to filter false matches. An initial putative matching step is time-consuming based on quick mismatches elimination and high inlier ratios. This initial matching step has challenged further for matching speeds improvement. Our new quick matching method decreases matching times significantly in terms of high inlier ratios. Method First, scale-invariant feature transform (SIFT) features are from image extraction and these feature points are sorted out in accordance with their scales to establish initial matches based on top 10% feature scales derived of NNDR threshold. Top-scale SIFT features is identified to extract the initial matches due to small quantity and high quality. Qualified matches can be obtained based on these features. Next, it samples regularly spaced feature coordinates in the query image. Initial matches based affine model estimation is conducted to transform the sampled points to the target image. The extraction of initial match accuracy is relatively high in terms of top-scale feature points. Thus, accuracy of the affine model estimated from this match is also high. The virtual center point (VCP) is targeted as center of a neighboring feature point set. The VCP is not represented the extracted feature position well as a search center for the neighboring feature points. A set of pairwise neighbors is obtained in terms of an inner rectangular window based feature points search of these pairwise sample points. Finally, feature point matching is performed independently within each pair of windows. Following the set of VCP neighbors derived of the range tree, correspondence is to be established within pairwise windows. As most features are grouped in small windows, feature correspondence can be established rapidly through traditional brute force (BF) matching. Thus, BF matching is utilized to implement feature matching in pairwise windows instead of a k-dimensional tree (kd-tree). The number of points assumed for each window considerably affects the performance of spatial divide and conquer (SDC) algorithm and it determines the number of VCPs and the size of the windows, consequently regulating the average number of feature points per window. This is significant for BF image matching procedures operating within the windows. To analyze the effect of window size on matching time and inlier ratio, a group of images are optioned with resolutions between 795×530 and 3 976×2 652 pixels. The demonstrations indicate that the window size is inversely proportional to running speed and inlier ratio. Result A multi-sensors based remote sensing data across China is obtained. These analyzed images are mentioned as bellows: 1) the Landsat 8 images of West Sichuan of China; 2) SPOT satellite images of Beijing; 3) GF-3 synthetic aperture radar (SAR) data for the southeast of Wuhan; 4) ZY-3 satellite data for Qingdao area of Shandong province. The images were sized between 816×716 and 2 976×2 273 pixels, and the maximum relative rotation angle was 30°. These images cover several types of geographical environment, including mountains, cities, coastal plains, forests, and farmland. Extensive experiments on various sizes and orientations of remote sensing images demonstrate that our proposed method is highly accurate and reduces the matching time by 1-2 orders of magnitude compared with traditional and the state-of-the-art methods. Conclusion A top-scale SIFT features based quick image matching method is illustrated and analyzed. Parallel computing can further improve the speed of our algorithm due to the independent matching procedures for separate windows.