Aluir Dal Poz 
  
AUTOMATIC SPACE RESECTION USING A CONSTRAINED RELATIONAL MATCHING 
Aluir Porfírio Dal Poz' 
Antonio Maria Garcia Tommaselli” 
"Sao Paulo State University - Pres. Prudente, SP - Brazil 
Department of Cartography 
{ aluir, tomaseli } @prudente.unesp.br 
Working Group III/1 
.KEY WORDS: Digital Photogrammetry, Straight Lines, Automation, Relational Matching, IEKF. 
ABSTRACT 
Image orientation is a basic problem in Digital Photogrammetry. While interior and relative orientations have been 
successfully automated, the absolute orientation (or space resection) continues to be an important topic for research. An 
approach has been developed to automate the absolute orientation based on relational matching and a heuristic that uses the 
analytical relation between image and object-space straight lines. A build-in self-diagnosis is also integrated in this 
method, involving the implementation of data snooping statistic test in the process of spatial resection using the Iterated 
Extended Kalman Filtering (IEKF). The aim of this paper is to present the basic principles of the proposed approach and 
results based on real data. 
1 INTRODUCTION 
Considerable progresses have been accomplished in the automation of several geometric tasks, as, for example, the interior 
and relative orientation and the generation of digital orthophoto. However, the situation is quite different for semantic 
tasks, like the linear feature extraction from digital images for GIS data capture and updating and absolute orientation. 
Concerning this last one, the main difficult is related to the fact that a correspondence process needs to be performed 
between a digital image and a symbolic model describing the ground control. In such a case, line-based methods are 
potentially better, because lines are easier to be detected in digital images and grouped than points. 
In this paper, we are interested in a special class of lines, i.e., the straight lines. A simpler photogrammetric model can be 
derived for this type of entity, whose complexity is similar to the well-known collinearity equation. Some straight line- 
based photogrammetric model have been developed and can be found, for example, in Tommaselli and Lugnani (1988), 
Mulawa and Mikhail (1988), Tommaselli and Tozzi (1996), and Tommaselli and Dal Poz (1999). 
Although linear features are easier to locate than points and can be determined with sub-pixel precision, the automatic 
feature extraction and correspondence is a difficult task to solve properly. A combination of several approaches is proposed 
in this paper. Image orientation is recursively improved using IEKF (Iterative Extended Kalman Filtering) and the feature 
extraction process is constrained by the filter feedback. This process was firstly applied in Machine Vision (Tommaselli 
and Tozzi, 1996) but the feature extraction and the matching methods were not suitable to aerial images. Since then, a 
relational matching method has been developed (Dal Poz et al, 1996; Dal Poz and Tommaselli, 1998) and feature 
extraction algorithm has been improved as well (Tommaselli and Dal Poz, 1999). 
The proposed solution is described in the section 2. The results based on real data are presented and discussed in the 
section 3. Finally, conclusions are given in the section 4. 
2 THE PROPOSED SOLUTION 
The basic input data are the digital image, the ground control groupings (A, ..., A,), and the interior and approximate 
exterior orientation parameters. Ground control groupings correspond to local structures (e.g., road crossing) and can be 
extracted by measuring two 3D endpoints by conventional field survey or by photogrammetric plotting. The automatic 
orientation process is carried out in three main steps. First, a ground control grouping is selected (e. g., A;) and its 
position in the image-space is predicted and the feature extraction process is applied only to a small window enclosing 
  
206 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000.