Volume XL-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 29-33, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-29-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 29-33, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-29-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Aug 2015

19 Aug 2015

TREATMENT OF GEODETIC SURVEY DATA AS FUZZY VECTORS

G. Navratil, E. Heer, and J. Hahn G. Navratil et al.
  • Department for Geodesy and Geoinformation, TU Vienna, Austria

Keywords: Fuzzy Vector, Geodetic Survey Data, Analysis

Abstract. Geodetic survey data are typically analysed using the assumption that measurement errors can be modelled as noise. The least squares method models noise with the normal distribution and is based on the assumption that it selects measurements with the highest probability value (Ghilani, 2010, p. 179f). There are environment situations where no clear maximum for a measurement can be detected. This can happen, for example, if surveys take place in foggy conditions causing diffusion of light signals. This presents a problem for automated systems because the standard assumption of the least squares method does not hold. A measurement system trying to return a crisp value will produce an arbitrary value that lies within the area of maximum value. However repeating the measurement is unlikely to create a value following a normal distribution, which happens if measurement errors can be modelled as noise. In this article we describe a laboratory experiment that reproduces conditions similar to a foggy situation and present measurement data gathered from this setup. Furthermore we propose methods based on fuzzy set theory to evaluate the data from our measurement.