Armin Gruen

Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)

General Topics and Scene Reconstruction.- An Overview of DARPAs Research Program in Automatic Population of Geospatial Databases.- A Testbed for the Evaluation of Feature Extraction Techniques in a Time Constrained Environment.- The Role of Artificial Intelligence in the Reconstruction of Man-Made Objects from Aerial Images.- Scene Reconstruction Research - Towards an Automatic System.- Semantic Modelling of Man-Made Objects by Production Nets.- From Large-Scale DTM Extraction to Feature Extraction.- Building Detection and Reconstruction.- 3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation.- A System for Building Detection from Aerial Images.- On the Reconstruction of Urban House Roofs from Aerial Images.- Image-Based Reconstruction of Informal Settlements.- A Model Driven Approach to Extract Buildings from Multi-View Aerial Imagery.- Automated Building Extraction from Digital Stereo Imagery.- Application of Semi-Automatic Building Acquisition.- On the Integration of Object Modeling and Image Modeling in Automated Building Extraction from Aerial Images.- TOBAGO - A Topology Builder for the Automated Generation of Building Models.- Crestlines Constribution to the Automatic Building Extraction.- Recognizing Buildings in Aerial Image.- Above-Ground Objects in Urban Scenes from Medium Scale Aerial Imagery.- Digital Surface Models for Building Extraction.- Extracting Artificial Surface Objects from Airborne Laser Scanner Data.- Interpretation of Urban Surface Models using 2D Building Information.- Least Squares Matching for Three Dimensional Building Reconstruction.- Assessment of the Effects of Resolution on Automated DEM and Building Extraction.- Road Extraction.- The Role of Grouping for Road Extraction.- Artificial Intelligence in 3-D Feature Extraction.- Updating Road Maps by Contextual Reasoning.- Fast Robust Tracking of Curvy Partially Occluded Roads in Clutter in Aerial Images.- Linear Feature Extraction with 3-D LSB-Snakes.- Context-Supported Road Extraction.- Map/GIS-Based Methods.- Three-Dimensional Description of Dense Urban Areas using Maps and Aerial Images.- MOSES: A Structural Approach to Aerial Image Understanding.- An Approach for the Extraction of Settlement Areas.- Extraction of Polygonal Features from Satellite Images for Automatic Registration: The ARCHANGEL Project.- Visualisation.- A Set of Visualization Data Needs in Urban Environmental Planning & Design for Photogrammetric Data.- A Virtual Reality Model of a Major International Airport.- Managing Large 3D Urban Database Contents Supporting Phototexture and Levels of Detail.- List of Workshop Participants.- Author Index.

Processing of Ikonos imagery for submetre 3D positioning and building extraction

Abstract An investigation of the application of 1-m Ikonos satellite imagery to 3D point positioning and building reconstruction is reported. The focus of the evaluation of Geo panchromatic imagery has been upon 3D positioning accuracy, radiometric quality and attributes of the image data for building feature extraction. Following an initial review of characteristics of the Ikonos system, the multi-image dataset employed is described, as is the Melbourne Ikonos testfield. Radiometric quality and image preprocessing aspects are discussed, with attention being given to noise level and artifacts, as well as to methods of image enhancement. The results of 2D and 3D metric accuracy tests using straightforward geometric sensor models are summarised, these showing that planimetric accuracy of 0.3–0.6 m and height accuracy of 0.5–0.9 m are readily achievable with only three to six ground control points. A quantitative and qualitative assessment of the use of stereo Ikonos imagery for generating building models is then provided, using the campus of the University of Melbourne as an evaluation site. The results of this assessment are discussed, these highlighting the high accuracy potential of Ikonos Geo imagery and limitations to be considered in building reconstruction when a comprehensive and detailed modelling is required.

CC-Modeler: a topology generator for 3-D city models

Abstract In this paper, we introduce a semi-automated topology generator for 3-D objects, CC-Modeler (CyberCity Modeler). Given the data as point clouds measured on Analytical Plotters or Digital Stations, we present a new method for fitting planar structures to the measured sets of point clouds. While this topology generator has been originally designed to model buildings, it can also be used for other objects, which may be approximated by polyhedron surfaces. We have used it so far for roads, rivers, parking lots, ships, etc. The CC-Modeler is a generic topology generator. The problem of fitting planar faces to point clouds is treated as a Consistent Labelling problem, which is solved by probabilistic relaxation . Once the faces are defined and the related points are determined, we apply a simultaneous least-squares adjustment in order to fit the faces jointly to the given measurements in an optimal way. We first present the processing flow of the CC-Modeler. Then, the algorithm of structuring the 3-D point data is outlined. Finally, we show the results of several data sets that have been produced with the CC-Modeler.

Road extraction from aerial and satellite images by dynamic programming

Abstract In this paper, we propose a semi-automatic road extraction scheme which combines the wavelet decomposition for road sharpening and a model-driven linear feature extraction algorithm based on dynamic programming. Semi-automatic means that a road is extracted automatically after some seed points have been given coarsely by the operator through activation of a mouse using a convenient interactive image-graphics user interface. With a wavelet transform interesting image structures can be enhanced and a multiresolution representation can be obtained by selection of a special wavelet. We have built a special wavelet for road sharpening, which has been implemented as a fast pyramidal algorithm. In the model-driven feature extraction scheme, a road is represented by a generic road model with six photometric and geometric properties. This model is formulated by some constraints and a merit function which embodies a notion of the “best road segment”, and evaluated by a “time-delayed” dynamic programming algorithm. The mathematical foundation and issues relating to its practical implementation are discussed in detail. This approach has been applied very successfully to extract complete road networks from single SPOT scenes and aerial images. Thereby the algorithm runs in a monoplotting mode, deriving X, Y, Z -coordinates of the roads, whereby the Z -component comes from real-time interpolation within an underlying DTM. Some experimental results are also given in this paper.

High-quality image matching and automated generation of 3D tree models

Image matching is a key procedure in the process of generation of Digital Surface Models (DSM). We have developed a new approach for image matching and the related software package. This technique has proved its good performance in many applications. Here, we demonstrate its use in 3D tree modelling. After a brief description of our image matching technique, we show results from analogue and digital aerial images and high‐resolution satellite images (IKONOS). In some cases, comparisons with manual measurements and/or airborne laser data have been performed. The evaluation of the results, qualitative and quantitative, indicate the very good performance of our matcher. Depending on the data acquisition parameters, the photogrammetric DSM can be denser than a DSM generated by laser, and its accuracy may be better than that from laser, as in these investigations. The tree canopy is well modelled, without smoothing of small details and avoiding the canopy penetration occurring with laser. Depending on the image scale, not only dense forest areas but also individual trees can be modelled.

Turning images into 3-D models

In this article developments and performance analysis of image matching for detailed surface reconstruction of heritage objects is discussed. Three dimensional image-based modeling of heritages is a very interesting topic with many possible applications. In this article we propose a multistage image-based modeling approach that requires only a limited amount of human interactivity and is capable of capturing the fine geometric details with similar accuracy as close-range active range sensors. It can also cope with wide baselines using several advancements over standard stereo matching techniques. Our approach is sequential, starting from a sparse basic segmented model created with a small number of interactively measured points. This model, specifically the equation of each surface, is then used as a guide to automatically add the fine details. The following three techniques are used, each where best suited, to retrieve the details: 1) for regularly shaped patches such as planes, cylinders, or quadrics, we apply a fast relative stereo matching technique. 2) For more complex or irregular segments with unknown shape, we use a global multi-image geometrically constrained technique. 3) For segments unsuited for stereo matching, we employ depth from shading (DFS). The goal is not the development of a fully automated procedure for 3D object reconstruction from image data or a sparse stereo approach, but we aim at the digital reconstruction of detailed and accurate surfaces from calibrated and oriented images for practical daily documentation and digital conservation of wide variety of heritage objects.

System Calibration Through Self-Calibration

The method of has proved to be one of the most powerful calibration techniques. If used in the context of a general bundle solution it provides for object space coordinates or object features, camera exterior and interior orientation parameters, and models other systematic errors as well. Therefore, because of its flexibility, it may be used in stereo, multi-frame systems, egomotion computations, etc. This chapter gives a brief introduction to the principle of self-calibration, emphasizes some of the problems which are associated with it, and demonstrates with practical data to what extent geometry and network design will influence the determinability of the self-calibration parameters. Finally, a system test will show the high accuracy performance of self-calibrating CCD camera systems.

Fundamentals of videogrammetry — A review

Abstract With the advent of inexpensive and powerful CCD- (‘video-’) cameras and associated image data transfer and processing hardware novel measurement systems, based on these components, are of great potential for human movement recording. This contribution gives an introduction to the fundamentals of ‘videogrammetry’, a measurement technique based on the principles of ‘photogrammetry’. We will discuss the concept of the multi-image (more than two CCD-frames) measurement mode, the bundle adjustment with its variants for point positioning, orientation and calibration, the notions of precision and reliability, system aspects including some details on CCD-cameras and image data transfer, calibration and self-calibration, algorithms for high precision image measurements, and finally show a typical application example. Besides being a highly automated measurement technique, videogrammetry provides for high accuracy (a relative accuracy of 1: 10000 can be reached in trajectory determination with standard components) and truly real-time data processing capabilities. In addition, a great number of particles (> 100) can be measured and tracked simultaneously, and surfaces and their deformation can be determined. Therefore, the videogrammetric technique is of great interest for applications in biomechanics, sport, animation, and virtual reality generation and control.

High-precision image matching for digital terrain model generation

Abstract The Adaptive Least Squares Correlation (ALSC), combining gray-level correlation with geometric conditions, was applied to a grid-sampling mode for generation of Digital Terrain Models (DTM) from aerial stereo models. The heights of targets of different geometric and contrast quality in large-scale digitized images were determined using this technique and compared to results from manual measurements. Both solutions showed an average difference of 0.016–0.038% and a maximum differences of 0.037–0.120% flying height (hg), depending on the height approximation. Most cases investigated required an initial height approximation at the 1–2% hg level. An average height error of 0.2% hg was cleared in each iteration step. The constraints imposed proved to define the movement of the gray-level image patches, thus limiting the search area and probability of false correlation, and increasing the precision and reliability of height determination.

Reality-based generation of virtual environments for digital earth

Abstract Digital Earth essentially consists of 3D and moreD models and attached semantic information (attributes). Techniques for generating such models efficiently are required very urgently. Reality-based 3D modelling using images as prime data source plays an important role in this context. Images contain a wealth of information that can be advantageously used for model generation. Images are increasingly available from satellite, aerial and terrestrial platforms. This contribution briefly describes some of the problems which we encounter if the process of model generation is to be automatised. With the help of some examples from Digital Terrain Model generation, Cultural Heritage and 3D city modelling we show briefly what can be achieved. Special attention is directed towards the use of model helicopters for image data acquisition. Some problems with interactive visualisation are discussed. Also, issues surrounding R&D, professional practice and education are also addressed.