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Title: Analysis of error introduced during end-user post-processing of airborne laser data (LiDAR)
Author: Smith, Sarah Louise
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2005
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The primary aims and objectives of this thesis are to identify the sources and operation of the errors which are introduced during end-user post-processing of airborne laser scanning data. Previous research has concentrated on the errors incorporated during data capture and preliminary supplier processing. The errors which are introduced by the end-users have been largely neglected. As a result, data users cannot currently estimate the errors within, and therefore the quality of, the models they produce. Laser scanning is a remote sensing technique for the capture of height data of the surface of the Earth. It offers competitive capture costs, high accuracy, and is particularly suited to capturing information in complex urban areas. As a result the commercial value of laser scanning data is high. However, in order to realise the potential of this technique, the quality of the datasets derived from the data must be assessed and the errors introduced during modelling understood. For users to make informed decisions regarding the design of their post-processing workflow it is fundamental that they know how and where errors may be introduced. The characteristics of these errors are investigated in this thesis using a range of approaches. End-user post-processing is divided into three techniques in the thesis: data structuring, filtering and segmentation. Each process is investigated hi terms of accuracy and sensitivity, through the comparison of several methods with reference models. New algorithms for filtering and segmenting laser data are presented. The errors created by each process are identified and analysed. The location of errors across the elevation surface are also investigated. It is shown how this information could be used to aid end-users design their post-processing methodology. The methodology for analyzing the errors is presented as a framework which could be used as a standard for ALS models. This thesis shows that the choice of post-processing methodology can significantly alter both the magnitude and spatial pattern of errors with a model derived from airborne laser scanning data. The differences between modeling strategies, and the importance of these differences, is shown with reference to a flood modeling application. Finally, strategies for minimizing error for post-processing are proposed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available