Assessing the transferability of airborne laser scanning and digital aerial photogrammetry derived growing stock volume models

Three-dimensional (3D) data from airborne laser scanning (ALS) and, more recently, digital aerial photogrammetry (DAP) have been successfully used to model forest attributes. While multi-temporal, wall-to-wall ALS data is not usually available, aerial imagery is regularly acquired in many regions. Thus, the combination of ALS and DAP data provide a sufficient temporal resolution to properly monitor forests. However, field data is needed to fit new forest attribute models for each 3D data acquisition, which is not always affordable. In this study, we examined whether transferability of growing stock volume (GSV) models may provide an improvement in the efficiency of forest inventories updating. We used two available ALS datasets acquired with different characteristics in 2009 and 2010, respectively, generated two DAP point clouds from imagery collected in 2010 and 2017, and utilized field data from two ground surveys conducted in 2009 and 2016-2017. We first analyzed the stability of point cloud derived metrics. Then, Support Vector Regression models based on the most stable metrics were fitted to assess model transferability by applying them to other datasets in four different cases: (1) ALS-ALS, (2) DAP-DAP temporal, (3) ALS-DAP and (4) ALS-DAP temporal. Some metrics were found to be enough stable in each case, so they could be used interchangeably between datasets. The application of models to other datasets resulted in unbiased predictions with relative root mean square error differences ranging from -8.27% to 14.59%. Results demonstrated that 3D-based GSV models may be transferable between point clouds of the same type as well as point clouds acquired using different technologies such as ALS and DAP, suggesting that DAP data may be used as a cost-efficient source of information for updating ALS-assisted forest inventories.     

大区域无人机影像数据的快速几何处理

无人机遥感影像快速几何处理被广泛应用于灾害应急监测与评估、军事侦察等领域。针对目前多数商业软件所依赖的硬件平台成本昂贵、软件开发过程复杂等问题,面向通用硬件平台,提出一种大区域无人机影像数据的快速几何处理方法。对制约处理效率的各关键算法进行优化和改造,实现大区域无人机影像数据的快速几何处理;在影像连接点匹配、正射纠正和影像镶嵌的处理过程中,均应用基于多核CPU并行处理技术,提高整体处理效率。多组无人机影像数据处理试验表明,该方法能够大幅提高处理效率,对硬件平台要求低,具有推广应用价值。     

3D Motion parameters determination based on binocular sequence images

Exactly capturing three dimensional (3D) motion information of an object is an essential and important task in computer vision, and is also one of the most difficult problems. In this paper, a binocular vision system and a method for determining 3D motion parameters of an object from binocular sequence images are introduced. The main steps include camera calibration, the matching of motion and stereo images, 3D feature point correspondences and resolving the motion parameters. Finally, the experimental results of acquiring the motion parameters of the objects with uniform velocity and acceleration in the straight line based on the real binocular sequence images by the mentioned method are presented.     

基于语义的倾斜影像单体建模技术研究

针对应用倾斜航空影像进行自动化建模所创建的建筑物建模存在诸如模型细节缺失,难以获取语义信息的问题,该文提出了一种采用基于语义的建模方式构建建筑物模型的方法;首先对密集图像匹配(DIM)点云进行建筑物类提取,实现场景中建筑和其它地物的分离;然后进行基于点云分割和建筑物验证的建筑物单体标注,以保证对单体建筑物的有效标记;最后利用倾斜航空影像和DIM点云的建筑物各组件的语义识别和提取算法,实现以单体建筑物为建模对象的模型重建;同时,设计适用于建筑物重建的语义建模框架,对单体语义模型进行自动化纹理映射,构建出语义化的建筑物模型。该文提出的建模关键技术方法将有助于减少城市建模成本,提高建模效率,为智慧城市基础数据的建设提供关键技术储备,并发挥相应的的社会和经济效益。     

大重叠度无人机遥感影像的三维建模方法研究

探讨了无人机遥感系统获取的遥感影像的三维建模方法 ,应用相对定向直接进行无人机遥感影像相对定向 ,并通过实验分析了相邻影像间重叠度对相对定向精度的影响。基于高重叠度的无人机遥感影像 ,为提高同名点识别的精度 ,提出了多幅影像核线约束方法。实验证明本文是利用无人机遥感影像特点实现三维建模的有意义的尝试     

基于无人机遥感影像拓扑分析的三维重建

在无需任何地面控制点或其它先验知识前提下,探索一种基于无人机遥感影像的三维重建方法.利用无人机飞控数据建立的影像拓扑结构,依次通过特征点提取、影像匹配、从运动恢复结构等步骤估计出相机位置和姿态参数,并恢复出场景特征点云信息,最后对重建精度进行分析.试验结果表明,文中提出的方法可快速、可靠地实现较高精度的三维模型重建.     

结合影像的LIDAR数据三维建筑物提取

随着LIDAR技术的出现,三维建筑物的提取也受到越来越多的重视。由于LIDAR数据分布的不连续性和不规律性,直接从机载激光扫描测距数据中进行建筑物提取较为困难。本文提出了一种结合灰度影像的LIDAR数据三维建筑物提取方法,分三个步骤:首先对灰度影像进行建筑物二维提取;然后将处理后影像和LIDAR数据粗匹配,初步确定LI-DAR数据中的三维建筑物区域;最后利用一组阈值操作进行三维建筑物的精确提取。实验结果表明该方法简单实用,适应性强。