CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: DETECTION OF BUILDINGS AT AIRPORT SITES USING IMAGES & LIDAR DATA AND A COMBINATION OF VARIOUS METHODS Demir, N., Poli, D., Baltsavias, E.

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 72 Few commercial software packages allow automatic terrain, tree and building extraction from Lidar data. In TerraSCAN, a TIN is generated and progressively densified, the extraction of off-terrain points is performed using the angles between points to make the TIN facets and the other parameter is the distance to nearby facet nodes (Axelsson, 2001). In SCOP++, robust methods operate on the original data points and allow the simultaneous elimination of off-terrain points and terrain surface modelling (Kraus and Pfeifer, 1998). In summary, most approaches try to find objects using single methods. In our strategy, this study suggests complying different methods using all available data with the focus on improving the results of one method by exploiting the results from the remaining ones. 3. INPUT DATA AND PREPROCESSING The methods presented in this paper have been tested on a dataset of the Zurich airport. The available data for this region are: 3D vector data of airport objects, colour and CIR (Colour InfraRed) images, Lidar DSM/DTM data (raw and grid interpolated). The characteristics of the input data can be seen in Table 1. Image Data RGB CIR Provider Swissphoto Swissphoto Scale 1: 10*000 1: 6*000 Scan Resolution 14.5 microns 14.5 microns Acquisition Date July 2002 July 2002 Ground Sampling Distance (GSD) (cm) 14.5 cm 8.7 cm Lidar Data DSM DTM Provider Swisstopo Swisstopo T yP e Raw & grid Raw & grid Raw point density & Grid 1 pt / 2 sqm & 1 pt / 2 sqm & Spacing 2m 2m Acquisition Date Feb. 2002 Feb.2002 Vector data Only for validation purposes Provider Unique Co. Horizontal / Vertical Accuracy (2 sigma) 20 / 25 cm Table 1. Input data characteristics. The 3D vector data describe buildings (including airport parking buildings and airport trestlework structures). It has been produced from stereo aerial images using the semi-automatic approach with the CC-Modeler software (Gruen and Wang, 1998). Some additional reference buildings outside the airport perimeter were collected using CIR images with stereo measurement by using LPS software. The images have been firstly radiometrically preprocessed (noise reduction and contrast enhancement), then the DSM was generated with the software package SAT-PP, developed at the Institute of Geodesy and Photogrammetry, ETH Zurich (Zhang, 2005). For the selection of the optimum band for matching, we considered the GSD, and the quality of each spectral channel based on visual checking and histogram statistics. Finally, the NIR band was selected for DSM generation. The final DSM was generated with 50cm grid spacing. Using this DSM, CIR orthoimages were produced with 12.5cm ground sampling distance. Lidar raw data (DTM and DSM) have been acquired with “leaves off’. The DSM point cloud includes all Lidar points (including points on terrain, tree branches etc.). The DTM data includes only points on the ground, so it has holes at building positions and less density at tree positions. The height accuracy (one standard deviation) is 0.5 m generally, and 1.5 m at trees and buildings, the latter referring only to the DSM. The grid DSM and DTM were interpolated from the original raw data by Swisstopo with the Terrascan commercial software. 4. BUILDING DETECTION Four different approaches have been applied to exploit the information contained in the image and Lidar data, extract different objects and finally buildings. The first method is based on DSM/DTM comparison in combination with NDVI (Normalised Difference Vegetation Index) analysis for building detection. The second approach is a supervised multispectral classification refined with height information from Lidar data and image-based DSM. The third method uses voids in Lidar DTM and NDVI classification. The last method is based on the analysis of the density of the raw DSM Lidar data. The accuracy of the building detection process was evaluated by comparing the results with the reference data and computing the percentage of data correctly extracted and the percentage of reference data not extracted. 4.1 DSM/DTM and NDVI (Method 1) By subtracting the DTM from the DSM, a so-called normalized DSM (nDSM) is generated, which describes the above-ground objects, including buildings and trees. As DSM, the surface model generated by SAT-PP and as DTM the Lidar DTM grid were used. NDVI image has been generated using the NIR and R bands. A standard unsupervised (ISODATA) classification was used to extract vegetation from NDVI image. The intersection of the nDSM with NDVI should correspond to trees. By subtracting the resulting trees from the nDSM, the buildings are obtained. 83% of building class pixels were correctly classified, while all of 109 buildings have been detected but not fully, the omission error is 7% . Within the detected buildings, some other objects, such as aircrafts and vehicles, were included. The extracted buildings are shown in Figure 1. Figure 1. Building detection result from method 1. (Left: airport buildings. Right: residential area). 4.2 Supervised classification and use of nDSM (Method 2) The basic idea of this method is to combine the results from a supervised classification with the height information contained in the nDSM. Supervised classification methods are preferable to unsupervised ones, because the target of the project is to detect well-defined standard target classes (airport buildings, airport corridors, bare ground, grass, trees, roads, residential houses, shadows etc.), present at airport sites. The training areas were selected manually using AOI (Area Of Interest) tools within the ERDAS Imagine commercial software (Kloer, 1994). Among the available image bands for classification (R, G and B from colour images and NIR, R and G bands from CIR images), only the bands from CIR images were used due to their better resolution and the presence of NIR channel (indispensable for

Access restriction

Copyright

fullscreen: CMRT09

Monograph

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation