排序方式: 共有63条查询结果,搜索用时 15 毫秒
1.
针对基于仿射不变特征的遥感影像匹配技术,提出了一种自动优化方法,以进一步提高匹配准确性.根据典型需求形成了两套优化实施方案,基于所提出的自动优化方法实现了相应具体算法.针对不同类型的多组影像,自动优化的效果与相应方案的预定目标一致,充分证明了本方法的有效性与适用性. 相似文献
2.
针对AKAZE算法在无人机影像匹配过程中存在的匹配精度低和稳定性较差问题,本文提出一种基于多匹配策略融合的改进影像匹配方法。该方法首先对影像降采样并利用AKAZE算法检测多尺度特征。然后采用一种稳定的RootSIFT描述符进行特征描述。其次,融合最近邻距离比值、双向匹配和余弦相似度约束匹配策略进行特征匹配以降低误匹配率。最后,采用随机抽样一致性(RANSAC)算法确定最终的特征对应关系,并求得几何变换模型。实验结果表明,该方法在获得更多正确匹配点对的同时具有较高的匹配正确率和精度,能够更好适用于无人机影像匹配。 相似文献
3.
4.
The extraction of object features from massive unstructured point clouds with different local densities, especially in the presence of random noisy points, is not a trivial task even if that feature is a planar surface. Segmentation is the most important step in the feature extraction process. In practice, most segmentation approaches use geometrical information to segment the 3D point cloud. The features generally include the position of each point (X, Y and Z), locally estimated surface normals and residuals of best fitting surfaces; however, these features could be affected by noisy points and in consequence directly affect the segmentation results. Therefore, massive unstructured and noisy point clouds also lead to bad segmentation (over-segmentation, under-segmentation or no segmentation). While the RANSAC (random sample consensus) algorithm is effective in the presence of noise and outliers, it has two significant disadvantages, namely, its efficiency and the fact that the plane detected by RANSAC may not necessarily belong to the same object surface; that is, spurious surfaces may appear, especially in the case of parallel-gradual planar surfaces such as stairs. The innovative idea proposed in this paper is a modification for the RANSAC algorithm called Seq-NV-RANSAC. This algorithm checks the normal vector (NV) between the existing point clouds and the hypothesised RANSAC plane, which is created by three random points, under an intuitive threshold value. After extracting the first plane, this process is repeated sequentially (Seq) and automatically, until no planar surfaces can be extracted from the remaining points under the existing threshold value. This prevents the extraction of spurious surfaces, brings an improvement in quality to the computed attributes and increases the degree of automation of surface extraction. Thus the best fit is achieved for the real existing surfaces. 相似文献
5.
基于RFM的高分辨率卫星遥感影像自动匹配研究 总被引:5,自引:0,他引:5
摘 要:提出一种基于有理多项式模型(RFM)进行高分辨率卫星遥感影像自动匹配的方法。首先利用RFM进行高分辨率卫星影像直接定位和同名点预测;然后基于投影轨迹建立近似核线方程,并分析了核线精度;接着采用金字塔影像策略进行核线约束的近似一维影像匹配,并经最小二乘影像匹配精化匹配结果;最后采用RANSAC算法剔除误匹配点以获取最终的匹配结果。通过与二维灰度相关方法和SIFT匹配方法的比较试验,证明本文方法可靠性好、匹配成功率高,较好地解决了多时相、大姿态角高分辨率卫星遥感影像的自动匹配难题。 相似文献
6.
7.
8.
针对BRISK特征检测算法在遥感影像中匹配时同名点对冗余度高和全局性差等特点,考虑BRISK特征检测算法能获取大量无人机遥感影像特征点,Delaunay三角网算法能够利用影像的BRISK特征点的粗匹配点对构建三角网,本文综合两种算法的优点,提出了一种结合BRISK特征检测算法和Delaunay三角网算法的剔除无人机遥感影像误匹配点对方法。该方法利用两张影像的BRISK粗匹配特征点构建Delaunay三角网,利用遍历两张影像三角网中的三角形相似度剔除错误匹配点对,并利用摄影不变量原理进一步剔除误匹配点对,提高了两张影像的精度;对比分析了Delaunay三角网的射影不变量算法,RANSAC算法分别剔除原始影像组、加入椒盐噪声影像组及旋转影像组的BRISK特征误匹配点对的效果。试验结果表明,3组影像分别利用结合BRISK特征和Delaunay三角网的射影不变量算法的无人机遥感影像匹配方法获得的正确特征匹配点对冗余度低、全局性优。 相似文献
9.
采用RANSAC算法剔除观测数据中的离群值,再使用线性内插法进行补全,利用整体投影计算的思想提取两点间的相对重力值,并对其精度和标准差进行检验。结果表明,动态重力观测的残差最大值为4.641 μGal,重复性标准差最大值为4.384 μGal,均优于5 μGal。该方法可获得较高精度的重力观测数据,为在复杂环境下获取相对重力值提供一种新方法。 相似文献
10.
《地震研究进展(英文)》2021,1(3):100008
In the presence of background noise, arrival times picked from a surface microseismic data set usually include a number of false picks that can lead to uncertainty in location estimation. To eliminate false picks and improve the accuracy of location estimates, we develop an association algorithm termed RANSAC-based Arrival Time Event Clustering (RATEC) that clusters picked arrival times into event groups based on random sampling and fitting moveout curves that approximate hyperbolas. Arrival times far from the fitted hyperbolas are classified as false picks and removed from the data set prior to location estimation. Simulations of synthetic data for a 1-D linear array show that RATEC is robust under different noise conditions and generally applicable to various types of subsurface structures. By generalizing the underlying moveout model, RATEC is extended to the case of a 2-D surface monitoring array. The effectiveness of event location for the 2-D case is demonstrated using a data set collected by the 5200-element dense Long Beach array. The obtained results suggest that RATEC is effective in removing false picks and hence can be used for phase association before location estimates. 相似文献