Wide-angle airborne laser range data analysis for relative height determination of ground-based benchmarks

 A wide-angle airborne laser ranging system has been developed for the determination of relative heights of ground-based benchmarks in regional-scale networks (typically 100 laser reflectors spread over 100 km²). A first prototype demonstrated a 1–2 mm accuracy in radial distance measurement in a ground-based experiment in 1995. The first aircraft experiment was conducted in 1998, over a small area (1 km²) equipped with a network of 64 benchmarks. The instrument was modified before that experiment, in order to minimize echo superimposition due to the high density of benchmarks. New data processing algorithms have been developed, for the deconvolution of strongly overlapped echoes and a high a priori uncertainty in the aircraft flight path, and for the estimation of benchmark coordinates. A special methodology has been developed for the parameterization of these algorithms and of outlier detection tests. From a total of 2×10⁴ pseudo-range measurements, that have been acquired from two flights composed of 30 legs each, only 3×10³ remain after outlier detection. A positioning accuracy of 1.5 cm in the vertical coordinate (2.1 cm in the difference between the two flights) has been achieved. It is shown that the errors are normally distributed, with a nearly zero mean, and are consistent with the a posteriori uncertainty. It is also shown that the accuracy is limited mainly by the sensitivity of the photodetector used for this experiment (due to reduced response time). Another limiting factor is the effect of aircraft attitude changes during the measurements, which produces additional uncertainties in absolute distance measurements. It is planned to test new photodetectors with high internal gains. These should provide, in future experiments with smaller benchmark density, an improvement in signal-to-noise ratio of a factor of 5–10, leading to sub-centimeter vertical positioning accuracy. Received: 19 June 2001 / Accepted: 3 January 2002     

Rapid texture mapping from image sequences for building geometry model

An effective approach,mapping the texture for building model based on the digital photogrammetric theory, is proposed. The easily-ac-quired image sequences from digital video camera on helicopter are used as texture resource, and the correspon-dence between the space edge in build-ing geometry model and its line feature in image sequences is determined semi-automatically. The experimental re-sults in production of three-dimension-al data for car navigation show us an attractive future both in efficiency and effect.     

Rapid texture mapping from image sequences for building geometry model

An effective approach, mapping the texture for building model based on the digital photogrammetric theory, is proposed. The easily-acquired image sequences from digital video camera on helicopter are used as texture resource, and the correspondence between the space edge in building geometry model and its line feature in image sequences is determined semiautomatically. The experimental results in production of three-dimensional data for car navigation show us an attractive future both in efficiency and effect.     

人卫激光测距望远镜系统的指向修正

利用实测数据分析了人卫激光测距望远镜系统常用的两种指向修正模型(球谐函数模型与Mount Strom lo支架模型)的精度和稳定性。结果表明,两种模型均能达到较好的拟合精度,但Mount Strom lo支架模型系数间大多不相关,其稳定性明显优于球谐函数模型。     

基于资源三号影像的建筑物高度信息提取方法

建筑物高度信息的获取是高分辨率遥感影像信息提取研究中的热点问题之一。该文提出了一套结合面向对象分类方法的建筑物高度反演技术方法。首先,利用建筑物和阴影的形态学指数,通过面向对象分类方法提取建筑物轮廓和阴影信息;然后,采用相交线平均法计算阴影长度;最后,根据阴影长度和建筑物高度的几何关系模型计算建筑物高度。采用西安市的国产资源三号(ZY-3)卫星遥感数据进行提取试验,通过171栋建筑物的实际测量高度对结果进行验证,获得了91.23%的总体精度,显示出该方法在建筑物高度信息提取研究方面具有一定的现实意义。     

支持向量机的建筑物激光脚点提取方法

本文提出了一种基于全色波段航空影像和激光雷达数据的建筑物检测方法.如何从激光点云数据中提取出建筑物激光脚点,是建筑物三维重建和轮廓提取的难点问题之一.植被密集区域以及与建筑物紧密相邻的树木的激光点很难与建筑物激光点区分开.本文利用支持向量机对单个激光点的特征进行两分类,特征向量包括激光点的高程、高程变化信息以及与激光点...     

Research on laser range scanning and its application

1　ＩｎｔｒｏｄｕｃｔｉｏｎＩｎｒｅｃｅｎｔｙｅａｒｓｌａｓｅｒｒａｎｇｅｓｃａｎｎｅｒ ,ｗｈｉｃｈｉｓｏｎｅｒｅｐｒｅｓｅｎｔａｔｉｖｅｏｆｔｈｅｈｉｇｈａｎｄｎｅｗｔｅｃｈｎｏｌｏｇｙｉｎ ｄｕｓｔｒｉｅｓ,ｈａｓｂｅｅｎｍａｋｉｎｇ ｇｒｅａｔｐｒｏｇｒｅｓｓｉｎｒｅａｌ＿ｔｉｍｅａｃｑｕｉｓｉｔｉｏｎｏｆｍｕｌｔｉ＿ｌｅｖｅｌｏｆ 3Ｄｓｐａｔｉａｌｏｂｊｅｃｔ.Ａｉｒｂｏｒｎｅｓｙｓｔｅｍ ,ｃｏｍｂｉｎｅｄｗｉｔｈｏｔｈｅｒｐｏｓｉｔｉｏｎｉｎｇｔｅｃｈｎｏｌｏｇｙｓｕｃｈａｓＧＰＳ ,ＩＮＳ …     

On filtering and compressing lageos laser range data

A method of compressing LAGEOS laser range measurements into normal points is described. First, the raw range measurements are screened, on a pass-by-pass basis, by a filter which utilizes the fact that the range varies with time very nearly like a quadratic curve. Second, the time series of the filtered range data over each pass is divided into small segments, typically of 150 s duration. Third, each separate segment is modelled by a unique low-order Chebyshev polvnomial, typically of order four. Fourth, the polynomial is interpolated at some instant, typically that corresponding to the mean time of the segment, to vield the normal point. Baseline measurements across Australia obtained from full rate data and from data compressed into 150 s normal points, agree to within the measurement error. These differences are mainly due to the effects of data distribution and pass geometry. Computer costs are substantially reduced.     

A laser scanning-based method for fast estimation of seismic-induced building deformations

Monitoring damaged buildings in an area where an earthquake has occurred requires the use of techniques which provide rapid and safe measurements even in emergency conditions. In particular, remote sensing techniques like terrestrial laser scanning (TLS) can satisfy these requirements, since they produce very dense point clouds in little time and also allow an accurate geometric modeling of observed buildings. Nevertheless, strong constraints on TLS data acquisition geometry, such as acquisition distance and incidence angles, typically characterize an area in seismic emergency conditions. In order to correctly interpret the data, it is necessary to estimate errors affecting TLS measurements in these critical conditions. A reliable estimation can be achieved by means of experiments and numerical simulations aimed at quantifying a realistic noise level, with emphasis on reduction of artifacts due to data acquisition, registration and modeling. This paper proposes a data analysis strategy in which TLS-based morphological maps computed as point-to-primitive differences are created. The method can be easily used for accurate surveying in emergency conditions. In order to demonstrate the proposed method in very diverse situations, it was applied to rapidly detect deformation traces in the San Giacomo Roncole Campanile (Modena), the Asinelli tower (Bologna) and the Cantalovo Church (Verona), three buildings damaged by the Mw 5.9 Emilia Romagna 2012 earthquake (Italy).     

An automatic mosaicking method for building facade texture mapping using a monocular close-range image sequence

This paper presents an automatic mosaicking method for generating building facade textures from a monocular close-range digital image sequence. The process begins with the computation of the camera parameters (except the coordinates of the projective center), which are determined by combining vanishing point geometry with constraints of a straight line bundle as well as prior information of parallel lines in object space. The raw images are later rectified for the purpose of eliminating their salient geometric distortion. Next, automatic retrieval of the relevant image segment is implemented using the detecting range variance by means of the histogram of projective differences between the corresponding points for each of the facades from the raw image sequence. A strip model of the least-squares adjustment, which is similar to the strip block adjustment in aerial triangulation, is employed to determine the spatial alignment of each of the image segments in order to generate the facade textures from the relevant image segments. Afterwards, the entire building facade texture is mosaicked by ortho-image generation. Two refining strategies are proposed to optimize the mosaic result. One is refining the mosaic region where corresponding points are difficult to match but plenty of horizontal lines are available, and the constraint of corresponding horizontal lines is introduced to implement this process. The other strategy is to refine the unsatisfactory mosaic region by densifying the corresponding points by means of the spatial alignment of the relevant image segment computed by the strip method. The experimental results indicate that this method is widely applicable and compares well with other reported approaches with regard to automation level and applicability, for uncalibrated images as well as images with large geometric distortions.     

一种传统民居遥感提取方法

针对基于地物光谱统计特征的建筑物提取方法由于存在较大的同物异谱现象导致提取结果不满足要求的问题,该文提出了一种基于形态学建筑物指数并顾及纹理特征的遥感提取方法。该方法综合考虑传统民居在高分辨率遥感影像上的光谱、形态和纹理特征,首先利用形态学建筑物指数法提取建筑,并使用最小矩形长宽比和像元个数区分道路和零星地物,而后利用Contourlet变换和谱直方图相似性计算进行纹理甄别,实现传统民居的遥感识别和提取。为了验证该方法,选取湖南省常宁市庙前镇中田村QuickBird影像进行试验,结果表明该方法能够获得较高精度的提取结果,整体精度为71.54%,影响提取精度的关键原因为损毁严重的建筑物光谱特征与目标图像纹理相差较大。     

Generalization of 3D building texture using image compression and multiple representation data structure

Textures are an essential part of 3D building models and often consume large portions of the data volume, thus making visualization difficult. Therefore, we propose a multi-resolution texture generalization method to compress the textures of 3D building models for dynamic visualization at different scales. It consists of two steps: image compression and texture coloring. In the first step, texture images are compressed using wavelet transformation in both the horizontal and the vertical direction. In the second step, a TextureTree is created to store building texture color for dynamic visualization from different distances. To generate a TextureTree, texture images are iteratively segmented by horizontal and vertical dividing zones, until each section is basically in the same color. Then the texture of each section is represented by their main color and the TextureTree is created based on the color difference between the adjacent sections. In dynamic visualization, the suitable compressed texture images or the TextureTree nodes are selected to generate 3D scenes based on the angle and the distance between the viewpoint and the building surface. The experimental results indicate that wavelet based image compression and the proposed TextureTree can effectively represent the visual features of the textured buildings with much less data.     

Optimisation of building detection in satellite images by combining multispectral classification and texture filtering

Conventional multispectral classification methods show poor performance with respect to detection of urban object classes, such as buildings, in high spatial resolution satellite images. This is because objects in urban areas are very complicated with respect to both their spectral and spatial characteristics. Multispectral classification detects object classes only according to the spectral information of the individual pixels, while a large amount of spatial information is neglected. In this study, a technique is described which attempts to detect urban buildings in two stages. The first stage is a conventional multispectral classification. In the second stage, the classification of buildings is improved by means of their spatial information through a modified co-occurrence matrix based filtering. The direction dependence of the co-occurrence matrix is utilised in the filtering process. The method has been tested by using TM and SPOT Pan merged data for the whole area of the city of Shanghai, China. After the co-occurrence matrix based filtering, the average user accuracy increased by about 46% and the average Kappa statistic by about 57%. This result is about 26% better than the accuracy improvement through normal texture filtering. The method presented in this study is very useful for a rapid estimation of urban building and city development, especially in metropolitan areas of developing countries.     

Stereo analysis of high-resolution SAR images for building height estimation in cases of orthogonal aspect directions

SAR stereo image analysis for 3D information extraction is mostly carried out based on imagery taken under same-side or opposite-side viewing conditions. For urban scenes in practice stereo is up to now usually restricted to the first configuration, because increasing image dissimilarity connected with rising illumination direction differences leads to a lack of suitable features for matching, especially in the case of low or medium resolution data. However, due to two developments SAR stereo from arbitrary viewing conditions becomes an interesting option for urban information extraction. The first one is the availability of airborne sensor systems, which are capable of more flexible data acquisition in comparison to satellite sensors. This flexibility enables multi-aspect analysis of objects in built-up areas for various kinds of purpose, such as building recognition, road network extraction, or traffic monitoring. The second development is the significant improvement of the geometric resolution providing a high level of detail especially of roof features, which can be observed from a wide span of viewpoints. In this paper, high-resolution SAR images of an urban scene are analyzed in order to infer buildings and their height from the different layover effects in views taken from orthogonal aspect angles. High level object matching is proposed that relies on symbolic data, representing suitable features of urban objects. Here, a knowledge-based approach is applied, which is realized by a production system that codes a set of suitable principles of perceptual grouping in its production rules. The images are analyzed separately for the presence of certain object groups and their characteristics frequently appearing on buildings, such as salient rows of point targets, rectangular structures or symmetries. The stereo analysis is then accomplished by means of productions that combine and match these 2D image objects and infer their height by 3D clustering. The approach is tested using real SAR data of an urban scene.     

Two algorithms for extracting building models from raw laser altimetry data

Two new techniques for the determination of building models from laser altimetry data are presented. Both techniques work on the original laser scanner data points without the requirement of an interpolation to a regular grid. Available ground plan information may be used, but is not required. Closed solutions for the determination of the parameters of a standard gable roof type building model based on invariant moments of 2 1/2-D point clouds are shown. In addition, the analysis of deviations between point cloud and model does allow for modelling asymmetries such as dorms on a gable roof. By intersecting planar faces nonparametric buildings with more complex roof types can also be modelled. The techniques were applied to a FLI-MAP laser scanner dataset covering an area of 500×250 m² with a density of more than 5 points/m². Within this region, all but one building could be modelled. An analysis of the variance of the parameters within a group of buildings indicates a precision in the range of 0.1–0.2 m.     

A combined single range and single image device for low-cost measurement of building façade features

The dimensions of building façades and window apertures are usually determined by making direct measurements using tapes and plummets. This approach, however, has a number of drawbacks including the physical risk to which the persons making the measurements are exposed. This paper proposes an indirect approach based on close range photogrammetry that is inexpensive, simple, fast and safe, and which does not require specialist staff or direct ground control measurements. The method is based on taking a picture using a digital camera and measuring the distance to the object using a handheld laser distance meter. Both items of equipment are mounted on a specially designed support that allows the laser distance meter to move independently of the camera.     

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.     

一种三维激光点云中建筑物立面渐进分割方法

针对建筑物立面分割的问题,该文提出了一种三维激光点云中建筑物立面渐进分割方法。建立三维格网索引,通过分析建筑物立面在三维格网内的空间分布特征和二维平面格网内投影的线性分布特征,确定立面种子格网和投影线的拟合点,拟合投影线并基于种子格网约束生长完成每层格网中立面粗分割;使用RANSAC算法对粗分割后的立面点云进行面拟合,实现精细分割,并将各层格网中的立面进行合并,实现建筑物立面的完整分割。实验结果表明,该方法能有效地实现建筑物立面的精细化分割,有助于后续的建筑物精细三维模型重建。     

Knowledge based reconstruction of building models from terrestrial laser scanning data

This paper presents an automatic method for reconstruction of building façade models from terrestrial laser scanning data. Important façade elements such as walls and roofs are distinguished as features. Knowledge about the features’ sizes, positions, orientations, and topology is then introduced to recognize these features in a segmented laser point cloud. An outline polygon of each feature is generated by least squares fitting, convex hull fitting or concave polygon fitting, according to the size of the feature. Knowledge is used again to hypothesise the occluded parts from the directly extracted feature polygons. Finally, a polyhedron building model is combined from extracted feature polygons and hypothesised parts. The reconstruction method is tested with two data sets containing various building shapes.     

基于回光强度的地面三维激光扫描仪测距误差模型

地面三维激光扫描仪是一种新型测量仪器,其核心优势是非接触、速度快、精度高,应用领域非常广泛,而使用之前的设备检校极为重要。影响地面三维激光扫描仪测距精度的因素很多,本文的研究以地面三维激光扫描仪测距原理及误差来源为基础,提出一种稳健的曲线拟合算法对点云数据进行处理,建立了基于回光反射强度的地面三维激光扫描仪测距误差模型,经实验验证取得了良好效果。