University of Twente Student Theses


Radiometric correction of mobile laser scanning intensity data

Oh, Donghyun (2010) Radiometric correction of mobile laser scanning intensity data.

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Abstract:The recent development of laser scanning technique has made it possible to record the intensity of received signals as well as to provide the accurate geometric information of a point cloud. Many studies show the potential of intensity data for a lot of applications (e.g. strip adjustment, segmentation and feature extraction). Meanwhile, the problems in intensity-based applications have been presented due to radiometric systematic bias and reflectance noise. Traditionally, the intensity has been corrected by the radar range equation in airborne laser scanning (ALS) data. However, it is found that these models are not sufficient to correct terrestrial laser scanning (TLS) data. Moreover, mobile laser scanning (MLS) data have not been thoroughly studied. This research proposes the correction model of MLS intensity data to be able to reduce its radiometric systematic bias by two main approaches: a) theoretical model-based approach and b) empirical modelbased approach. The empirical model-based approach is tested by two models: adjusted radar range equation and data fitting by polynomials. In the initial phase filtering of multi-echoes and outliers is carried out which are not related to radiometric systematic noise. The geometry of a remaining point cloud is reconstructed into the range and incidence angle which are the main influencing factors on intensity data. In the following phase the theoretical model-based approach corrects intensities by range and incidence angle that individual points have. The empirical model-based approach uses the correction functions dependent on the range and incidence angle, whereby the parameters are defined by samples from homogeneous surfaces. The evaluation is performed based on (a) consistency of intensities in a homogeneous surface, (b) consistency in the two sensors, (c) contrast between different surfaces and (d) local noise reduction of intensities. In the assessment result it is indicated that the radar range equation is not suitable for MLS data. Furthermore, it is enlightened that among tested methods the data fitting by polynomials clearly reduces the intensity variation in a homogeneous surface and different sensors, and makes the contrast significant between different surfaces. Also shown is that the correction by data fitting can be used in the applications such as feature extraction, classification and segmentation.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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