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:: A SEMI-AUTOMATIC APPROACH TO OBJECT EXTRACTION FROM A COMBINATION OF IMAGE AND LASER DATA S. A. Mumtaz, K. Mooney

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Voi. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 A SEMI-AUTOMATIC APPROACH TO OBJECT EXTRACTION FROM A COMBINATION OF IMAGE AND LASER DATA S. A. Mumtaz 3 '*, K. Mooney 3 3 Dept, of Spatial Information Sciences, The Dublin Institute of Technology, Bolton Street, Dublin 1, Ireland (salman.mumtaz, kevin.mooney)@dit.ie Commission III, WG III/4 KEY WORDS: LiDAR, Object Extraction, Data fusion, Buildings, Trees, Roads ABSTRACT: The aim of the authors’ research is to develop an automated or semi-automated workflow for the extraction of objects such as buildings, trees and roads for noise mapping and road safety purposes. The workflow must utilise national airborne spatial data available throughout the country and be capable of robust incorporation in the noise modelling systems of a national roads authority. This paper focuses on the extraction of multiple objects by fusing data captured by two independent sensors, namely the Leica ADS40 aerial camera and the Leica ALS50 airborne laser scanner (LiDAR). A workflow has been developed for the extraction of objects utilizing height values from a normalised DSM generated using LiDAR or aerial images, multiple LiDAR echo data and NDVI (Normalized Difference Vegetation Index) data computed from multispectral ADS40 data. Major tasks include LiDAR data classification, segmentation and its integration with the information extracted from aerial images. Buildings are extracted first and this facilitates the extraction of other objects. Preliminary results of this semi-automated process indicate high completeness rates for buildings trees and roads but 60% quality rates (e.g. buildings). Quality may be improved by manual extraction of small objects but continuing research is focussed on reducing reliance on such manual intervention. 1. INTRODUCTION The National Roads Authority (NRA) in Ireland is responsible for generating noise maps in the environment of roads used by more than 8220 vehicles per day. According to the EU noise directive this exercise must be repeated every five years. Inputs for generating the noise maps include terrain model, location and dimension of buildings, trees, noise barriers and the geometric properties of roads. Capturing this data using field surveys or digital images is time consuming and expensive, especially if the same exercise must be repeated every five years. It is the intention of this work that all required objects be extracted using automatic or semiautomatic techniques from LiDAR and aerial image data of the type available from the National Mapping Agency of Ireland, OSi (Ordnance Survey of Ireland). Later the extracted information can be easily combined and analyzed along with noise data in a GIS system. For noise mapping, building detail or tree models are not required. Buildings or trees boundaries with height information are sufficient. High resolution image and LiDAR sensors (ADS40 & ALS50) were used to capture the data for a part of County Sligo in the northwest of Ireland. Digital images were captured in April 2007 with a ground resolution of 15 cm. LiDAR data were captured separately in May 2007 at a flying height of 1241 m with a swath width of 800 m, resulting in an average point density of approximately 2 points/m 2 . The ALS50 sensor recorded position, multiple echoes and intensity of the returning pulse. The area selected for processing is about 3 km 2 and is covered by a single image strip and four LiDAR strips. This eliminates the necessity for bundle block adjustment and ground control point acquisition. The reason for relying completely on direct geo-referencing in this research is the fact that in many situations ground control points may not be available. Strip adjustment of the LiDAR data was performed using the Terra Match application from TerraSolid. 1.1 Motivation In recent years, research on automated object extraction has increased because of the increased use of GIS (Geographical Information Systems) with the consequential need for data acquisition and update. Digital Photogrammetry is considered to be one of the most precise methods for capturing large scale data for GIS analysis from high resolution aerial images. However, it requires significant resources to digitize all objects of interest. As detailed high resolution digital images are regularly acquired as part of the national programme of OSi, it is considered important to develop automatic or semi-automatic techniques to exploit their potential for applications such as noise modelling involving the extraction of objects such as buildings, trees and roads. LiDAR can provide high density 3D point clouds in a very short time with acceptable horizontal and high vertical accuracy. OSi also acquires national LiDAR data using the ALS50 sensor from Leica Geosystems. The availability to the national roads authority of Ireland of both of these high resolution data sources provides the impetus for this research. However, the development in sensor technology is far more rapid than the advancements in automatic or semi automatic object extraction. Moreover there is still a large gap between * Salman Ali Mumtaz

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