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

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 DETECTION OF BUILDINGS AT AIRPORT SITES USING IMAGES & LIDAR DATA AND A COMBINATION OF VARIOUS METHODS Demir, N. 1 , Poli, D. 2 , Baltsavias, E. 1 1 - (nusret,manos@geod.baug.ethz.ch) Institute of Geodesy and Photogrammetry, ETH Zurich, CH-8093, Zurich, Switzerland 2- (daniela.poli@jrc.ec.europa.eu) European Commission - Joint Research Center, Ispra (VA), Italy KEY WORDS: DTMs/DSMs, Lidar Data Processing, Multispectral Classification, Image Matching, Information Fusion, Object Detection, Buildings ABSTRACT: In this work, we focus on the detection of buildings, by combining information from aerial images and Lidar data. We applied four different methods on a dataset located at Zurich Airport, Switzerland. The first method is based on DSM/DTM comparison in combination with NDVI analysis (Method 1). The second one is a supervised multispectral classification refined with a normalized DSM (Method 2). The third approach uses voids in Lidar DTM and NDVI classification (Method 3), while the last method is based on the analysis of the density of the raw Lidar DTM and DSM data (Method 4). An improvement has been achieved by fusing the results of the different methods, taking into account their advantages and disadvantages. Edge information from images has also been used for quality improvement of the detected buildings. The accuracy of the building detection was evaluated by comparing the results with reference data, resulting in 94% detection and 7% omission errors for the building area. 1. INTRODUCTION In this work, we focus on the building detection for airport sites. The acquisition of a reliable geospatial reference database of airports, and in particular the automatic extraction of buildings and obstacles at airports, both have a critical role for aviation safety. Often, 3D information of airports is not available, not accurate enough, not complete, or not updated. Thus, methods are needed for generation of accurate and complete 3D geodata with high degree of automation. In particular, buildings and trees are considered as obstacles, so they should be correctly extracted. In this work, we focus on the detection of buildings, as a first step for their 3D extraction. There are several methods applied for this purpose, based on image and/or airborne Lidar data. In our approach, buildings are detected in aerial images and Lidar data through multiple methods using multispectral image classification, DSM (Digital Surface Model) and DTM (Digital Terrain Model) comparisons and density analysis of the raw Lidar point cloud. The detection quality is improved by a combination of the results of the individual methods. This paper will give a brief overview of the related work on this subject. Then, after the description of the test area at Zurich Airport, Switzerland, the strategy and methodology will be presented and the results will be reported, compared and commented. This work is a part of the EU 6 th Framework project PEGASE (Pegase, 2009). 2. PREVIOUS WORK Aerial images and Lidar data are common sources for object extraction. In digital photogrammetry, features of objects are extracted using 3D information from image matching or DSM/DTM data, spectral, textural and other information sources. Pixel-based classification methods, either supervised or unsupervised, are mostly used for land-cover and man-made structure detections. For the classical methods e.g. minimum- distance, parallelepiped and maximum likelihood, detailed information can be found in (Lillesand and Kiefer, 1994). In general, the major difficulty in using aerial images is the complexity and variability of objects and their form, especially in suburban and densely populated urban regions (Weidner and Foerstner, 1995). Regarding Lidar, building and tree extraction is basically a filtering problem in the DSM (raw or interpolated) data. Some algorithms use raw data (Sohn and Dowman, 2002; Roggero, 2001; Axelsson, 2001; Vosselman and Maas, 2001; Sithole, 2001; Pfeifer et al., 1998), while others use interpolated data (Elmqvist et ah, 2001; Brovelli et ah, 2002; Wack and Wimmer, 2002). The use of raw or interpolated data can influence the performance of the filtering. The algorithms differ also in the number of points they use at a time. In addition, every filter makes an assumption about the structure of bare-earth points in a local neighbourhood. This assumption forms the concept of the filter (Sithole and Vosselman, 2003). The region-based methods use mostly segmentation techniques, like in Brovelli et ah (2002), or using Hough transformation (Tarsha-Kurdi et ah, 2007). Some researchers use 2D maps as prior information for building extraction (Brenner, 2000; Haala and Brenner., 1999; Durupt and Taillandier, 2006; Schwalbe et ah, 2005). Topographic maps provide outlines, classified polygons and topologic and 2D semantic information (Elberink and Vosselman, 2006). In general, in order to overcome the limitations of image-based and Lidar-based techniques, it is of advantage to use a combination of these techniques. Sohn and Dowman (2007) used IKONOS images to find building regions before extracting them from Lidar data. Straub (2004) combines information from infrared imagery and Lidar data to extract trees. Rottensteiner et ah (2005) evaluate a method for building detection by the Dempster-Shafer fusion of Lidar data and multispectral images. They improved the overall correctness of the results by fusing Lidar data with multispectral images.

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