Full text: Proceedings of the Symposium on Global and Environmental Monitoring (Part 1)

Malcolm E. Fuller & Manfred Ehlers 
Department of Surveying Engineering 
University of Maine 
Orono, Maine 04469-0110 
E-mail: ehlers@mecanl.bitnet 
In the integration of remotely sensed multisensor, multitemporal, multispectral, and 
multiresolution image data, one of the more time-consuming tasks has been the rectification 
of different images to a mutual geometric basis. This is due to the difficulty of automating 
the process of selecting adequately distributed and identifiable control points. This paper 
outlines such an automated process involving: control point identification, matching, and 
correlation. Result of preliminary tests applied to the system are also reported. 
KEY WORDS: image matching, Moravec operator, partitioning, correspondence, 
correlation, fine tuning. 
An important step in the integration of imagery is placing 
images in exact pixel by pixel registration with each other. 
There are many integrative applications (e.g. modeling 
global environmental cycles, multitemporal change detection, 
GIS integration) which require this process. However, to 
date it has remained one of the few image processing 
functions which have not been automated. Image registration 
still requires, to a large degree, time-consuming human input 
for the selection and matching of control points (CPs) in the 
images to co-register. 
An automated image registration approach would make the 
study of multisensor, multitemporal, multiresolution, and 
multispectral image datasets much more efficient. This is 
particularly true as the size of these datasets increase to 
include more information, and computers become powerful 
enough to handle such a large quantity of data. 
Our research in the area of image registration has been 
directed toward developing a prototype software package 
which will select, match, and fine tune CPs with as little 
human interactive input as possible (i.e. the user should only 
have to enter input/output file names along with some 
processing parameters). This paper will explain the approach 
taken for this process, as well as a description of the 
prototype software package as it currently stands. 
Preliminary test results are also included. 
In this paper, an image which is already in a desired 
geometric basis (e.g. rectified to UTM coordinates) is called 
the reference image. An image which is not in a desired 
geometry (e.g. raw scanned data), and must be transformed 
in order to match the reference image is called the subject 
image.The initial strategy taken toward the registration of a 
subject image to a reference image is similar to one described 
by Luhmann and Ehlers [1984] in the development of an 
image matching system. The process of co-registering 
images is broken up into six steps: 
1 .Preprocessing of the images, which may include filtering, 
scale change, window definition and other corrections to 
enhance the matching process. 
2.Selection of candidate control points (CCPs) in each 
image to co-register. 
3. Matching of the CCPs in each image, to determine which 
points in the reference image correspond to those in the 
subject image. 
4. Fine tuning of the matched CCPs to determine the actual 
location of points in the subject image to subpixel accuracy. 
5. Calculation of the rectification coefficients for the 
transformation from reference to subject image using the 
fine-tuned control points (CPs). 
6. Resampling of the subject image into the geometry of the 
reference image. 
In all of these steps except the first and the last, CCPs which 
have been determined to be ambiguous or otherwise 
unsuitable as control points are eliminated from further 
consideration. At the end of the process there should be 
considerably fewer CCPs than were chosen initially, but 
those points should be adequate for high-precision image 
CCP selection 
A human interpreter normally tries to choose control points 
in locations which contain easily identifiable surrounding 
texture and contrast (e.g. road intersections, bridges, 
building corners). Most attempts to automate the selection 
process rely on functions, called interest operators, which 
also locate areas in a digital image with a high surrounding 
contrast. Unlike a human interpreter, interest operators do 
not attempt to find CCPs which are topographically 
significant [Piechel 1986]. Suitable CCPs extracted by 
interest operators may be located in areas where a human 
would not normally select control points, yet show enough 
local contrast in the algorithm's eye for possible use. 
There are several interest operators that have been developed 
for use in extracting suitable points. Most of these can be 
found in digital photogrammetry and computer vision 
literature [Moravec 1980, Nagel 1981, Forstner 1985, 
Piechel 1986, Luhmann and Altrogge 1986]. Although 
originally developed for robotic vision applications, the 
Moravec operator was implemented first in our software 
package because it has been shown in previous research to

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