Full text: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

ISPRS, Vol.34, Part 2W2, "Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
1 Wanshou JIANG 2 Guo ZHANG 1 Deren LI 
Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing 
Wuhan University, #129 Luoyu road, Wuhan, P R. China 
Tel: +86-139-71187089, Fax: 86-27-87863229, E-mail: wsiws@china.com 
2 School of Information Engineering in Remote Sensing, Wuhan University, PR.China 
KEY WORDS: interior orientation, self-adaptive algorithm, softassign, deterministic annealing 
Automatic interior orientation is one of the key problems in photogrammetric processing of aerial images. In past years, it has been 
deeply studied, but it is still worth studying, especially when the images with fiducial marks are not good. In this paper, a new 
self-adaptive algorithm is put forward. It adopts softassign and deterministic annealing technology combined with affine transformation 
to search fiducial marks in metric images. Even when fiducial marks are not good, the Self-Adaptive Algorithm can still work successfully. 
Other strategies such as pyramid, automatic determination of searching range are also taken into consideration. 
Interior orientation is a fundamental problem in photogrammetry. 
Nowadays, most of the commercial software of DPS will be 
compelled to apply manual interior orientation if the automatic 
orientation is failed, which will make the users feel inconvenient. 
Interior orientation is usually referred to reestablishing the 
relationships between the pixels and the image coordinate 
system. We are concerned with the relationship of a set of 
(usually six affine) parameters for the transformation from pixels 
to image coordinates [1]. The pixel coordinate system of the 
digital image is explicitly given through the matrix of gray values. 
However, the image coordinate system is only given by the 
fiducial marks. Therefore, the transformation between pixel and 
image coordinates by fiducial marks has to be accomplished. 
The reestablishment of the interior orientation can be considered 
as a pattern recognition problem: one has to find the center of 
the pattern representing the fiducial marks and ascribe each 
found pattern the correct fiducial number. 
Listed in Table 1 are several traditional methods of interior 
orientation [2][3][4][5][6], In which, interior orientation are divided 
into three tasks: 
1) Approximate positioning of fiducial marks 
2) Subpixel positioning of fiducial centers 
3) Computation of transformation parameters 
Among these tasks, approximate positioning is the most 
important and the hardest. Gray correlation, binary correlation or 
other binary image analysis techniques is used in these methods 
to approximate position of fiducial marks. For the images 
photographed by RC10, RMK and other aerial cameras, these 
methods perform well. The fiducial marks of these cameras are 
distributed on the edges of image. No objects are imaged around 
the fiducial marks. And so no noise or few noises are assumed 
in these methods. The target detected by these methods will be 
only one. And the target is regarded as the fiducial marks 
But in close range photogrammetry, images are photographed 
with cameras such as P31, UMK etc. The fiducial distribution of 
P31 is similar to Fig. 1, in which the fiducial marks usually merge 
in the image of objects. Even worse, some parts of the objects 
(such as a mesh) may have the similar shape as the fiducial 
marks. Approximate positioning the fiducial marks with these 
methods may fail in such cases. If similar objects exist near a 
fiducial mark, several targets will be detected. Even if no similar 
targets exist, it is very hard for binary analysis to detect fiducial 
marks in complex gray images. As for gray correlation, the 
coefficient on fiducial can be very small. In out tests, some are 
smaller than 0.3. So the orientation of close range 
photogrammetry remains a problem. 
Fortunately, global image matching technique has achieved 
great successfully. In fact, interior orientation can be regarded as 
a global matching problem. Firstly we can use gray correlation to 
find several peaks of correlation coefficient in the predicted 
searching area. And then determining the fiducial marks from the 
points of peak is a combination optimization problem. Compared 
with other image matching problems, searching for the camera 
fiducial marks is relatively simple, and an affine transformation 
There are many algorithms such as genetic algorithm, relaxation 
algorithm and Hopfield networks, which are usually used to solve 
combination optimization problem. Relaxation algorithm and 
Hopfield networks generate local minima and do not usually 
guarantee that correspondences are one-to-one. Genetic 
algorithm is time consuming. To overcome these problems, 
softassign and deterministic annealing technology with affine 
transformation have been put forward [7][8]. This algorithm solely 
makes use of point location information, but it can supply an 
access to one-to-one correspondence and reject a fraction of 
points as outliers. In our study, softassign and deterministic 
annealing are adopted to solve the problem automatic interior 
In order to be more efficient in locating the fiducial marks with 
less effort, several levels of pyramid images and the original for 
each patch can be used throughout the template matching 
processing. The relationship of fiducial marks can be used in 
determining the searching area of the fiducial marks. When such 
an area has been determined, we can select several points 
whose correlation coefficients are locally maximal as candidates 
of the fiducial marks in the searching area. 
Table 1.Methods to automatic interior orientation 
Approximate fiducial positioning 
Accurate fiducial positioning 
Pose estimation 
Kersten and Haring (1995) 
Modified Hough transform 
Least-squares matching 
Depends on camera type 
Lue (1995) 
Grey value correlation hierarchy 
Least-squares matching 
Schickler (1995) 
Binary correlation hierarchy 
Grey value correlation 
Strackbein and Henze (1995) 
Binary image analysis, no 
Fitting of parabolas to gray 
value function 

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