集成SIFT算法与检测模型优化的UAV影像匹配方法

UAV Image Matching Method Integrating SIFT Algorithm and Detection Model Optimization

低空无人机(UAV)测量凭借着低成本、高效率、高精度的数据采集模式,可快速获取高空间分辨率的影像数据,已经成为遥感领域的一种重要技术手段。其中,影像匹配技术是UAV影像数据处理的重要步骤,图像间的匹配直接影响后期三维场景的精度及视觉效果。针对高原山地的高差起伏变化大地形复杂,植被覆被率高及地物分布不规则等问题存在,致使在该区域UAV地形测量处理中因局部噪声造成影像匹配较难。由于影像获取时受到该区特殊地形的限制,大场景影像需要借助多幅影像匹配拼接得到。目前,基于特征点的影像匹配是一种图像配准技术,不仅适用于低重叠度影像之间的匹配,还可以运用到运动恢复图像间的匹配。为探索特殊地形地貌条件下快速有效的UAV影像匹配技术,提出一种面向高原山地复杂地形的集成尺度不变特征变换(SIFT)算法与最近邻次近邻距离比(NNDR)、随机抽样一致算法(RANSAC)模型约束改进的UAV影像匹配方法。主要技术流程为:首先,基于SIFT算法,进行尺度空间的极值检测,构建高斯金字塔函数,通过高斯差分运算来实现特征点定位,并对所检测到的特征点的邻域位置、方向、尺度等进行统计分析,据此生成适合UAV影像特征的描述符;其次,集成“马式距离”和NNDR模型的综合运用,进行特征点对的第一次约束优化提取及相似度检测,在此基础上,利用RANSAC算法,引入匹配点对的均方根误差值(RMSE)进行第二次约束,以实现匹配错误点对的剔除,保证了影像匹配精确优化。此外,为了证实所提出优化算法的有效性,选择了1组高原山地典型地貌UAV影像数据进行匹配试验,结果表明:面向高原山地复杂地形进行无人机影像匹配中,所提出的改进算法不仅可以提取大量的特征点对,同时还可以提高同名特征点的检测正确率,并且配准正确率达到了85%,因此更加适用于高原山地复杂地形的无人机影像匹配处理技术优化。

Low-altitude unmanned aerial vehicle(UAV)measurement has become an important technical tool in the remote sensing field by the virtue of low-cost,high-efficiency,high-precision data acquisition mode and rapid acquisition images with high spatial resolution.Image matching technology is an important step in UAV image data processing,and the matching between images directly affects the accuracy and visual effect of the later 3 D scenes.For the highland mountainous area,the topography is complex with large elevation changes,high vegetation cover and irregular distribution of features,making it difficult to match the images due to local noise in the UAV topographic survey processing.As the special terrain of the area limits the image acuqisition,large scene images need to be obtained by matching and stitching multiple images.At present,feature point-based image matching is an image alignment technique,which is applicable to the matching between low overlap images and can be applied to the matching between motion recovery images.To explore the fast and effective UAV image matching technique under special terrain and landscape conditions.This paper proposes an integration Scale Invariant Feature Transform(SIFT)algorithm,the Nearest Neighbor Distance Ratio(NNDR)algorithm and Random Sample Consensus(RANSAC)model constraints improved the UAV image matching method for complex terrain in highland mountains.The main technical process is as follows:Firstly,based on the SIFT algorithm for extreme value detection in scale space,a Gaussian pyramid function is constructed,and feature point localization is achieved by a Gaussian difference operation.It also performs statistical analysis on the neighborhood location,direction,and scale of the detected feature points to generate a description suitable for UAV image features.Secondly,the first constraint of feature pairs is extracted,and similarity is detected by integrating the“Mahalanobis distance”and NNDR models.On this basis,the RANSAC algorithm is used to introduce the root mean square error(RMSE)of the matched pairs for the second constraint,to achieve the rejection of the wrong matched pairs and ensure the accurate optimization of image matching.In addition,to confirm the effectiveness of the optimization algorithm proposed in this paper,one group of UAV image data of typical landscapes in the highland mountains were selected for matching tests.The results show that the improved algorithm proposed in this paper can extract a large number of point pairs and improve the correct detection rate of the same name points in UAV image matching for complex terrain in highland mountainous areas.Moreover,the correct rate of alignment reaches 85%,so it is more applicable to the optimization of UAV image matching processing technology for complex terrain in highland mountains.