数字摄影测量像点量测精度检测方法研究

在摄影测量基本原理的基础上,提出通过构造理想标准式像对的方法,实现对全数字摄影测量系统的像点量测精度进行检测。以JX4数字摄影测量工作站为例,利用单张实景航片构造理想标准式像对,通过像点的理论坐标与实际采集坐标的较差统计精度,该检测方法对其它的数字摄影测量工作站具有普遍意义。     

数字摄影测量系统的数据获取

随着计算机技术的发展,摄影测量由模拟摄影测量和解析摄影测量发展到当今的数字摄影测量阶段,本文介绍了数字摄影测量的定义、主要的几种系统以及作业流程。     

利用视差进行立体像对的建成区检测方法

建成区的建筑物等地物通常高于周围地表,因此高度梯度在其边缘发生突变。从立体像对生成两幅核线影像及其对应的视差图,然后利用该规律由视差图生成立体像对视差指数(stereo pair disparity index,SPDI)图。对SPDI图的高值像素进行空间聚类,得到该视差图对应的核线影像中的建成区结果,并且该结果可投影定位于立体像对的原始影像。此外,对于立体像对的两幅影像中分别检测的不一致结果,由于视差图包含了同名点间的对应关系,因此利用视差图有利于对齐不一致结果。然后,对取交集可获得高可信度的、一致的建成区结果。最后,采用两组立体像对进行试验验证,结果表明该方法可从立体像对检测出高精度的建成区。     

近景摄影测量可视化摄影与处理管理

近景摄影中的场景较为复杂,通常不能采用常规航空摄影测量中的摄影方式,而是以单摄站旋转拍摄、多光束交会的方式进行。这种方式增加了摄影测量后续处理的难度,尤其是模型选取以及空三处理。介绍了一种简单实用的方法,通过机助的可视化界面建立摄影方案图,实现检索功能,简化数字近景摄影测量后续处理。     

全数字摄影测量系统VirtuoZo的应用

结合工作实践,介绍了全数字摄影测量系统VirtuoZo在数字化测图中一些新的应用方法。     

数字摄影测量基础实习的七步教学法

数字摄影测量是"摄影测量学"课程的重要内容,是摄影测量的发展方向。教学实习是重要的教学环节,介绍基于全数字摄影测量系统VirtuoZo的七步教学法,以使学生掌握全数字摄影测量系统的基本功能、一般作业流程和主要地图产品的制作过程。     

广义立体像对三维重建模型精度分析

本文依据基于多源遥感数据进行三维重建的思想,利用现有的对于某一区域,由不同传感器获取的高分辨率卫星影像,分别作为左右影像,建立起基于有理函数模型的严密空间前方交会数学模型,进而构建起广义立体像对,快速提取地面三维信息,实现目标物的三维重建。之后对重建的三维模型进行精度分析,并与利用传统双像立体像对重建的三维模型、以及单张影像重建的三维模型之间进行精度分析,比较三种不同方法所得到的三维模型的相对精度,并最终证明了利用广义立体像对进行地面目标的三维模型重建,其精度完全能满足生产要求。     

基于城市航空立体像对的全自动3维建筑物建模

城市建筑物3维建模是虚拟城市建设的关键技术之一。基于大比例尺航空立体像对提出3维建筑物建模方法。首先对2维边缘检测Canny算子进行改进，以期从城市航空影像中检测出较为精确的2维建筑物轮廓线。然后把改进的自适应最小二乘相关算法运用到金字塔匹配算法中，由于金字塔匹配算法中最为关键的问题是误差传递的有效控制，而以往的研究中尽管采用了多咱控制策略但效果不是很好，作者通过提出2种新的误差传播控制策略得到了较高精度的匹配结果。利用匹配生成的高精度3维信息对检测到的2维建筑物轮廓线进行3维插值，获得建筑物的3维信息，由此实现了建筑物3维建模。     

无人机航空摄影测量技术在矿区地形测量中的应用研究

无人机摄影测量是中小面积地形测绘的重要手段,具有低成本、高效率、高精度、信息量大、产品可以重复使用的优点,弥补了传统方法的不足.本文简要介绍了无人机航空摄影测量技术,从工程实例中,进一步论述了利用无人机,搭载非量测型相机,在地形复杂的高山矿区进行地形测量的过程和方法,评估了点位精度误差,希望能为相关领域研究人员提供借鉴...     

VirtuoZo全数字摄影测量系统制作DEM的实用方案

介绍利用VirtuoZo全数字摄影测量系统制作DEM的工作流程,说明生成DEM的具体操作步骤,指出提高作业精度的具体措施和办法.     

一种高郁闭度林分树高的估测方法

针对单一无人机影像无法有效地提取高郁闭度林分树高的问题,该文提出一种结合无人机影像数据和全站仪测量的地形数据获取高郁闭度林分树高的方法。①利用搭载数码相机的小型无人机平台,以50m航高获取实验区局部高精度林分影像,利用全站仪获取实验区的地形数据;②利用无人机影像处理软件对影像进行处理,通过初步的几何校正以及空三加密过程得到整个实验区的高分辨率DEM和DOM模型;③采用局部最大值算法探测单株林木的树冠中心点坐标,利用自然生长算法和高程差值公式得到树冠中心对应的树根高程;④以树冠中心点高程以及树根高程的差值作为单木树高的估计值。通过实验得出:结合无人机影像与全站仪数据能够准确快速地获取高郁闭度林分树高,本文提出的方法可以为森林可持续经营提供数据基础。     

Predicting biomass dynamics at the national extent from digital aerial photogrammetry

The demand for precise mapping and monitoring of forest resources, such as above ground biomass (AGB), has increased rapidly. National accounting and monitoring of AGB requires regularly updated information based on consistent methods. While remote sensing technologies such as airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have been shown to deliver the necessary 3D spatial data for AGB mapping, the capacity of repeat acquisition, remotely sensed, vegetation structure data for AGB monitoring has received less attention. Here, we use vegetation height models (VHMs) derived from repeat acquisition DAP data (with ALS terrain correction) to map and monitor woody AGB dynamics across Switzerland over 35 years (1983-2017 inclusive), using a linear least-squares regression approach. We demonstrate a consistent relationship between canopy height derived from DAP and field-based NFI measures of woody AGB across four inventory periods. Over the environmentally heterogeneous area of Switzerland, our models have a comparable predictive performance (R² = 0.54) to previous work predicting AGB based on ALS metrics. Pearson correlation coefficients between measured and predicted changes in woody AGB over time increased with shorter time gaps (< 2 years) between image capture and field-based measurements, ranging between 0.76 and 0.34. A close temporal match between field surveys and remote sensing data acquisition is thus key to reliable mapping and monitoring of AGB dynamics, especially in areas where forest management and natural disturbances trigger relatively fast canopy dynamics. We show that VHMs derived from repeat DAP capture constitute a cost effective and reliable approach to map and monitor changes in woody AGB at a national extent and can provide an important information source for national carbon accounting and monitoring of ecosystem service provisioning.     

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.     

基于极化相干层析技术的林分高度估算

极化相干层析(Polarization Coherence Tomography,PCT)技术可以通过不同极化状态下的干涉相干系数反演植被垂直结构分布,是SAR技术应用的一个重要发展方向。文中介绍极化相干层析的原理,总结极化相干层析技术重建垂直剖面的流程,通过仿真数据和ALOS PALSAR数据对极化相干层析技术在林分高度估算方面的适用性进行研究。估算结果表明,极化相干层析技术反演的林分高度特征信息准确,具有较强的鲁棒性。     

基于Landsat长时间序列数据估算树高和生物量

以Landsat长时间序列数据为研究对象,旨在以光谱序列信息反演森林参数为视角,应用Landtrendr算法从时间序列数据中提取森林扰动变量,使用随机森林计算方法建立扰动变量、反射率和GLAS激光点森林参数之间的关系模型,获取树高和生物量的空间分布信息。为多源遥感数据反演森林参数提供参考,研究证明基于Landsat长时间序列数据获得的森林扰动变量能够增强反射率和森林参数之间的相关性,可提高预测精度。     

ICESat-2机载试验点云滤波及植被高度反演

新一代星载激光雷达卫星ICESat-2将采用多波束微脉冲光子计数技术,并进行高程剖面式的对地观测。由于该点云数据具有背景噪声大、密度低并呈线状分布等特点,传统的点云滤波算法并不适用,研究新的点云滤波算法十分必要。本文以ICESat-2的机载模拟器MABEL数据为例,首先介绍了微脉冲光子计数激光雷达的基本原理和数据特点,并针对高程剖面点云提出基于局部距离统计和最小二乘局部曲线拟合的点云滤波算法;然后,对美国加利福尼亚州Sierras-Forest地区MABEL试验中532 nm通道的光子点云进行滤波处理,并利用识别的地面点插值得到3 m分辨率的线状DEM,进而估算了该区域美国云杉的平均树高;最后,对该滤波算法进行精度评价,并分析了误差来源及其对DEM精度和树高反演精度的影响。结果表明:(1)该算法整体精度达97.6%,能有效剔除绝大部分噪声点且对地形起伏具有较强的自适应能力;(2)误分噪声点影响了滤波过程中局部地形的拟合,而滤波过程中的分类误差将降低DEM和树高反演的精度。     

转换GPS高程的BP神经网络方法研究

对于目前大范围点位分布不均,拟合法高程转换存在效果失真、模型误差等问题,本文给出了改进的BP神经网络方法转换GPS高程为正常高的算法,并与曲面拟合方法比较分析。经实例验证,在较大范围内,用神经网络方法转换GPS高程优于二次曲面拟合方法,所获得的正常高可满足工程生产的精度要求,具有一定的实用价值。     

Sub-footprint analysis to uncover tree height variation using ICESat/GLAS

Detailed forest height data are an indispensable prerequisite for many forestry and earth science applications. Existing research of using Geoscience Laser Altimeter System (GLAS) data mainly focuses on deriving average or maximum tree heights within a GLAS footprint, i.e. an ellipse with a diameter of 65 m. However, in most forests, it is likely that the tree heights within such ellipse are heterogeneous. Therefore, it is desired to uncover detailed tree height variation within a GLAS footprint. To the best of our knowledge, no such methods have been reported as of now. In this study, we aim to characterize tree heights’ variation within a GLAS footprint as different layers, each of which corresponds to trees with similar heights. As such, we developed a new method that embraces two steps: first, a refined Levenberg–Marquardt (LM) algorithm is proposed to decompose raw GLAS waveform into multiple Gaussian signals, within which it is hypothesized that each vegetation signal corresponds to a particular tree height layer. Second, for each layer, three parameters were first defined: Canopy Top Height (CTH), Crown Length (CL), and Cover Proportion (CP). Then we extracted the three parameters from each Gaussian signal through a defined model. In order to test our developed method, we set up a study site in Ejina, China where the dominant specie is Populus euphratica. Both simulated and field tree height data were adopted. With regard to the simulation data, results presented a very high agreement for the three predefined parameters between our results and simulation data. When our methods were applied to the field data, the respective R² become 0.78 (CTH), CL (R² = 0.76), CP (R² = 0.74). Overall, our studies revealed that large footprint GLAS waveform data have the potentials for obtaining detailed forest height variation.     

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.     

多平台点云数据的单木参数提取精度分析

以浙江省海宁市4种代表行道树(广玉兰、无患子、悬铃木、香樟树)为研究对象,结合无人机(UAV)影像和三维激光扫描数据,利用ContextCapture、LiDAR360软件完成点云拼接、滤波、降噪和编辑,通过迭代最近点算法实现点云精细匹配,完成多平台点云数据融合,进而得到数字表面模型与数字高程模型,并制作冠层高度模型;采用分水岭分割算法对不同行道树树种的冠层高度模型进行单木分割,并综合局部最大值法实现单木树高、冠幅的参数提取。结果表明,本文方法进行行道树单木分割的精度高,树高、冠幅参数提取值的效果好,满足行道树几何参数调查要求。