Hossein Arefi

Building Reconstruction Using DSM and Orthorectified Images

High resolution Digital Surface Models (DSMs) produced from airborne laser-scanning or stereo satellite images provide a very useful source of information for automated 3D building reconstruction. In this paper an investigation is reported about extraction of 3D building models from high resolution DSMs and orthorectified images produced from Worldview-2 stereo satellite imagery. The focus is on the generation of 3D models of parametric building roofs, which is the basis for creating Level Of Detail 2 (LOD2) according to the CityGML standard. In particular the building blocks containing several connected buildings with tilted roofs are investigated and the potentials and limitations of the modeling approach are discussed. The edge information extracted from orthorectified image has been employed as additional source of information in 3D reconstruction algorithm. A model driven approach based on the analysis of the 3D points of DSMs in a 2D projection plane is proposed. Accordingly, a building block is divided into smaller parts according to the direction and number of existing ridge lines for parametric building reconstruction. The 3D model is derived for each building part, and finally, a complete parametric model is formed by merging the 3D models of the individual building parts and adjusting the nodes after the merging step. For the remaining building parts that do not contain ridge lines, a prismatic model using polygon approximation of the corresponding boundary pixels is derived and merged to the parametric models to shape the final model of the building. A qualitative and quantitative assessment of the proposed method for the automatic reconstruction of buildings with parametric roofs is then provided by comparing the final model with the existing surface model as well as some field measurements.

Accuracy Enhancement of ASTER Global Digital Elevation Models Using ICESat Data

Global Digital Elevation Models (GDEM) are considered very attractive for current research and application areas due to their free and wide range accessibility. The ASTER Global Digital Elevation Model exhibits the highest spatial resolution data of all global DEMs and it is generated for almost the whole globe. Unfortunately, ASTERGDEM data include many artifacts and height errors that decrease the quality and elevation accuracy significantly. This study provides a method for quality improvement of the ASTER GDEM data by correcting systematic height errors using ICESat laser altimetry data and removing artifacts and anomalies based on a segment-based outlier detection and elimination algorithm. Additionally, elevation errors within water bodies are corrected using a water mask produced from a high-resolution shoreline data set. Results indicate that the accuracy of the corrected ASTER GDEM is significantly improved and most artifacts are appropriately eliminated. Nevertheless, artifacts containing lower height values with respect to the neighboring ground pixels are not entirely eliminated due to confusion with some real non-terrain 3D objects. The proposed method is particularly useful for areas where other high quality DEMs such as SRTM are not available.

Iterative approach for efficient digital terrain model production from CARTOSAT-1 stereo images

This paper proposes a new algorithm for automatic digital terrain model (DTM) generation from high resolution CARTOSAT-1 satellite images. It consists of two major steps: generation of digital surface models (DSM) from stereo scenes and hierarchical image filtering for DTM generation. Automatic georeferencing, dense stereo matching, and interpolation into a regular grid yields a DSM. In the second step, the DSM pixels are classified into ground and nonground regions using an algorithm motivated from gray-scale image reconstruction to suppress unwanted elevated pixels. Nonground regions, i.e., 3D objects as well as outliers are iteratively separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Height profiles and comparisons between the generated DSM, derived DTM, and ground truth data are presented. The evaluation indicates that almost all nonground objects regardless of their size are eliminated and appropriate results are archived in hilly as well as smooth residential areas.

A hierarchical procedure for segmentation and classification of airborne LIDAR images

Airborne laser scanning has become an accepted technique for acquiring Digital Surface Models of the Earth surface. One of the major and still unsolved problems is the automatic separation of the topographic surface and 3D objects which cover the topographic surface. For this purpose a hierarchical segmentation procedure using morphological operations is developed and compared with more classical methods that are using morphological operations with a single structuring element to separate terrain from non-terrain surface models. The classical methods have a limited functionality in areas where a range of very small to very big 3D objects exists as well as in areas with a big variety of height differences. Starting point for the hierarchical process are morphological operations with different structuring element sizes applied to the Laser range data. For LIDAR systems which record first and last pulse both data sets are employed. The key of the segmentation process is to analyze the generated sequence of morphologically filtered data to extract ground points with high probability and separate them from nonground points. Aggregation to regions and the extraction of regions properties provide the basis for 3D object extraction. Further analysis focuses the feature description for the 3D regions which provides the input for classifying and separating 3D objects, in particular buildings and vegetation regions, from the ground surface regions. The local range variation, surface normal and NDVI features are utilized for evaluating the segmented regions. This procedure has been applied to a data set which was recorded by the TopScan laser scanning system with the density of about 1 point per square meter. Keywordslaser scanning; Mathematical morphology; segmentation; building extraction; trees extraction

Fusing stereo and multispectral data from WorldView-2 for urban modeling

Using the capability of WorldView-2 to acquire very high resolution (VHR) stereo imagery together with as much as eight spectral channels allows the worldwide monitoring of any built up areas, like cities in evolving states. In this paper we show the benefit of generating a high resolution digital surface model (DSM) from multi-view stereo data (PAN) and fusing it with pan sharpened multi-spectral data to arrive at very detailed information in city areas. The fused data allow accurate object detection and extraction and by this also automated object oriented classification and future change detection applications. The methods proposed in this paper exploit the full range of capacities provided by WorldView-2, which are the high agility to acquire a minimum of two but also more in-orbit-images with small stereo angles, the very high ground sampling distance (GSD) of about 0.5 m and also the full usage of the standard four multispectral channels blue, green, red and near infrared together with the additional provided channels special to WorldView-2: coastal blue, yellow, red-edge and a second near infrared channel. From the very high resolution stereo panchromatic imagery a so called height map is derived using the semi global matching (SGM) method developed at DLR. This height map fits exactly on one of the original pan sharpened images. This in turn is used for an advanced rule based fuzzy spectral classification. Using these classification results the height map is corrected and finally a terrain model and an improved normalized digital elevation model (nDEM) generated. Fusing the nDEM with the classified multispectral imagery allows the extraction of urban objects like like buildings or trees. If such datasets from different times are generated the possibility of an expert object based change detection (in quasi 3D space) and automatic surveillance will become possible.

Optimal Weight Design Approach for the Geometrically-Constrained Matching of Satellite Stereo Images

This study presents an optimal weighting approach for combined image matching of high-resolution satellite stereo images (HRSI). When the rational polynomial coefficients (RPCs) for a pair of stereo images are available, some geometric constraints can be combined in image matching equations. Combining least squares image matching (LSM) equations with geometric constraints equations necessitates determining the appropriate weights for different types of observations. The common terms between the two sets of equations are the image coordinates of the corresponding points in the search image. Considering the fact that the RPCs of a stereo pair are produced in compliance with the coplanarity condition, geometric constraints are expected to play an important role in the image matching process. In this study, in order to control the impacts of the imposed constraint, optimal weights for observations were assigned through equalizing their average redundancy numbers. For a detailed assessment of the proposed approach, a pair of CARTOSAT-1 sub-images, along with their precise RPCs, were used. On top of obtaining different matching results, the dimension of the error ellipses of the intersection points in the object space were compared. It was shown through analysis that the geometric mean of the semi-minor and semi-major axis by our method was reduced 0.17 times relative to the unit weighting approach.

Model-driven 3D building reconstruction based on integeration of DSM and spectral information of satellite images

Due to the recent improvements in satellite sensors and matching technology, the derivation of 3D models from space borne stereo data attached interests in various applications such as urban planning, telecommunication and tourism. Fully automatic 3D building reconstruction from space borne point cloud data is an active research topic, where the relatively low quality of Digital Surface Models (DSM) generated by stereo matching of satellite data comparing to LiDAR data. In order to establish an efficient method to achieve high quality models and complete automation from the mentioned DSM, a new method based on a model-driven strategy is proposed. For improving the results and better detection of building boundaries and corresponding ridgelines, footprints of the buildings and refined ortho-rectified panchromatic images are utilized as additional information. The presented results are promising, at least for larger buildings.

Statistically robust detection and evaluation of errors in DTMs

Digital Terrain Models (DTMs) have been an important topic in the study of ground surface landform, therefore precise evaluation of errors in DTMs production is a critical factor to assess the quality of DTM. In this paper the attribute of errors in DTMs are characterized and robust statistical methods are proposed as accuracy measure. A method based on robust statistical estimation is presented to detect gross errors in DTMs. For practical example a region in Catalonia, Spain, including city areas (Terrassa) as well as forest steep mountainous terrain (La Mola) is selected to evaluate the performance of DTM generation algorithm and to analyze the significance of errors for World view-1 satellite images.