ISPRS Commission III, Vol.34, Part 3A „Photogrammetric Computer Vision‘, Graz, 2002 
  
AUTOMATIC POSE ESTIMATION OF IMAGERY USING FREE-FORM CONTROL 
LINEAR FEATURES 
A. F. Habib”, S. W. Shin“, M. F. Morgan* 
“ Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 
470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA - (habib.1, shin.111, morgan.465)@osu.edu 
Commission III, WG III/1 
KEY WORDS: Data Fusion, Linear Features, Single Photo Resection, Matching, Robust Parameter Estimation and Change 
Detection 
ABSTRACT: 
Automatic Single Photo Resection (SPR) remains to be one of the challenging problems in digital photogrammetry. Visibility and 
uniqueness of distinct control points in the input imagery limit robust automation of the pose estimation procedure. Recent advances 
in digital photogrammetry mandate adopting higher-level primitives such as free-form control linear features for replacing traditional 
control points. Linear features can be automatically extracted from the image space. On the other hand, object space control linear 
features can be obtained from an existing GIS layer containing 3-D vector data such as road network, or from terrestrial Mobile 
Mapping Systems (MMS). In this paper, we present a new approach for simultaneously determining the position and attitude of the 
involved imagery as well as the correspondence between image and object space features. This approach does not necessitate having 
one to one correspondences between image and object space primitives, which makes it robust against changes and/or discrepancies 
between them. This characteristic will be helpful in detecting changes between object and image space linear features (e.g. due to 
temporal effects). The parameter estimation and matching follow an optimal sequential procedure that depends on the magnitude and 
direction of image space displacements resulting from incremental changes to the Exterior Orientation Parameters (EOP). 
Experimental results using real data proved the feasibility and robustness of our approach, especially when compared to those 
obtained through traditional manual procedures. Changes and/or discrepancies between the data sets are detected and highlighted 
  
through consistency analysis of the resulting correspondences. 
1. INTRODUCTION 
The majority of traditional computational procedures in 
photogrammetry rely on the correspondence between point 
primitives. With the recent advances in digital photogrammetry, 
more emphasis should be oriented towards using higher-level 
primitives in photogrammetric orientation procedures. There 
has been a substantial body of work dealing with the use of 
analytical linear features (e.g. straight lines and conic curves) in 
photogrammetric orientation (Habib et al, 2000b), (Habib, 
1999), (Mikhail, 1993), (Mulawa and Mikhail, 1988). On the 
other hand, very few papers addressed the use of free-form 
linear features (Zalmanson, 2000), (Habib and Novak, 1994). 
However, the suggested approaches by these authors assume the 
knowledge of the correspondence between the object and image 
space features. 
SPR is a photogrammetric procedure to determine the EOP of 
aerial images, which is a prerequisite task for variety of 
applications such as surface reconstruction, ortho-photo 
generation and object recognition. Traditionally, SPR is 
performed using signalised control points, which have to be 
established prior to the flight mission. Radiometric problems 
and small signal size in terms of number of pixels limit the 
robustness of the automation process (Gülch, 1994). Mikhail et 
al (1994) used radiometric models of the object space control 
points and tried to determine their instances in the image. Very 
good approximations of EOP are required in this approach to 
ensure small “pull-in” range. Other approaches (Haala and 
Vosselman, 1992; Drewniok and Rohr, 1997) employed 
relational matching of points. Relations between points are not 
as well-defined as those between linear or higher-level features. 
In this research, the SPR problem is solved using free-form 
linear features in the image and object space without knowing 
the correspondence between these entities. 
Presently, there is a great motivation for exploiting and 
integrating various types of spatial data. This motivation is 
fuelled by the availability of new acquisition systems such as 
aerial and terrestrial mobile mapping systems and airborne laser 
scanners. The suggested approach in this research, for automatic 
SPR, has the potential of incorporating object space information 
acquired from a terrestrial mobile mapping system, line maps, 
or a GIS database with aerial imagery. The fusion of these data 
will enable point-to-point correspondence between image and 
object space linear features. This type of correspondence 
facilitates change detection applications that are well suited for 
automation. The Modified Iterated Hough Transform (MIHT) 
for robust parameter estimation (Habib et al, 2000a) is used to 
estimate the EOP as well as matching image and object space 
points along the involved linear features. 
In the following section, a brief review of the traditional Hough 
transform, the newly developed MIHT for robust parameter 
estimation technique and its application in SPR are presented. 
In Section 3, the methodology of the suggested approach is 
outlined, including the optimum sequence for parameter 
estimation and change detection, followed by experimental 
results using real data. Finally, conclusions and 
recommendations for future research are presented. 
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