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Sensors 2014, 14(8), 13759-13777; doi:10.3390/s140813759

Confronting Passive and Active Sensors with Non-Gaussian Statistics

Department of Cartographic and Land Engineering, University of Salamanca, Polytechnic School of Avila. Hornos Caleros, 50, 05003, Avila, Spain
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Received: 21 May 2014 / Revised: 22 July 2014 / Accepted: 28 July 2014 / Published: 30 July 2014
(This article belongs to the Section Physical Sensors)
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Abstract

This paper has two motivations: firstly, to compare the Digital Surface Models (DSM) derived by passive (digital camera) and by active (terrestrial laser scanner) remote sensing systems when applied to specific architectural objects, and secondly, to test how well the Gaussian classic statistics, with its Least Squares principle, adapts to data sets where asymmetrical gross errors may appear and whether this approach should be changed for a non-parametric one. The field of geomatic technology automation is immersed in a high demanding competition in which any innovation by one of the contenders immediately challenges the opponents to propose a better improvement. Nowadays, we seem to be witnessing an improvement of terrestrial photogrammetry and its integration with computer vision to overcome the performance limitations of laser scanning methods. Through this contribution some of the issues of this “technological race” are examined from the point of view of photogrammetry. A new software is introduced and an experimental test is designed, performed and assessed to try to cast some light on this thrilling match. For the case considered in this study, the results show good agreement between both sensors, despite considerable asymmetry. This asymmetry suggests that the standard Normal parameters are not adequate to assess this type of data, especially when accuracy is of importance. In this case, standard deviation fails to provide a good estimation of the results, whereas the results obtained for the Median Absolute Deviation and for the Biweight Midvariance are more appropriate measures. View Full-Text
Keywords: passive sensor; active sensor; digital camera; laser scanner; non-Gaussian statistic; non-parametric statistic; measurement passive sensor; active sensor; digital camera; laser scanner; non-Gaussian statistic; non-parametric statistic; measurement
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Rodríguez-Gonzálvez, P.; Garcia-Gago, J.; Gomez-Lahoz, J.; González-Aguilera, D. Confronting Passive and Active Sensors with Non-Gaussian Statistics. Sensors 2014, 14, 13759-13777.

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