A ROBUST CHANGE DETECTION METHODOLOGY FOR TOPOGRAPHICAL 
APPLICATIONS 
G.A. Lampropoulos', Ting Liu* and C. Armenakis" 
* A.U.G. Signals Ltd., 1 St. Clair Avenue West, 11^ floor, Toronto, Ontario, M4V 1K7, 
Canada 
; lampro,tliu@augsignals.com 
Centre for Topographic Information, Geomatics Canada, Natural Resources Canada, 615 
Booth Str. 
Ottawa, Ontario K1A OE9 Canada 
Commission ThS - 13 
KEY WORDS: Distributed Processing, Change Detection, Feature Extraction, and Classification. 
ABSTRACT: 
In this paper, several classification methods are presented and the results are compared. The definition of "layer" and the method to 
create it are then introduced. Based on" layer", a multiple level change detection algorithm is proposed, which gives the details of 
the changes in each region and is demonstrated to be an easy, effective and reliable method. Experimental results are provided using 
RADARSAT images, which have been registered with the automated registration algorithm of A.U.G. Signals that is currently 
available under the distributed processing system www.signalfusion.com. 
1. INTRODUCTION 
Change detection is the process of identifying differences in the 
state of an object or phenomenon ‘by observing it at different 
times. It is useful in such diverse applications as land use 
change analysis, monitoring of shifting cultivation, assessment 
of deforestation, crop stress detection and so on. It is essential 
for studying changes on the earth's surface. Such changes may 
determine the rate of change for disaster management (e.g. 
flooding), ice monitoring, earthquake prediction and 
monitoring, urban planning etc. 
Remotely sensed data are now able to estimate changes with 
very high accuracy. The accuracy is proportional to the image 
resolution, i.e. the higher the resolution of the images used, the 
higher the accuracy of the change detection. There are several 
sensors used for change detection. SAR sensors offer the 
advantage of providing additional phase information that may 
be used for change detection. This is due to the fact that the 
pixels are complex numbers. When the pixel-to-pixel phase 
information is being used we say that this change detection 
process is based on interferometry. When only the amplitude of 
the images is used this process is called photogrammetric 
change detection. 
Change detection may be applied directly on images by using 
only the pixel amplitude or both the magnitude and phase, or 
transformed pixel values. The well-known change detection 
techniques are image differencing, image ratioing, image 
regression, Principal Component Analysis (PCA), wavelet 
772 
decomposition, change vector analysis and so on. In 
topographic change detection, for example if we want to 
study changes in a region where the water level changes, we 
are interested in studying only the changes between the two 
regions (land or water) [1, 2]. Hence, all land pixels may be 
assigned one value and all the water pixels another value. In 
this case, study of changes is much easier and all unnecessary 
image land or water information has been eliminated through 
an image segmentation transformation. 
To detect the changes for each region, classification should 
be performed first. There exist many classification methods. 
In this paper, we used three methods, which are thresholding, 
fuzzy C-mean and decision tree. 
The remainder of the paper is organized as follows. A 
detailed topographic change detection method based on 
region classification is described in Section 2. The definition 
of "layer" is introduced in Section 3. Section 4 discusses the 
distributed computing technique. Some simulations are given 
in Section 5. In Section 6, the conclusions of the paper are 
drawn. 
2. REGION CLASSIFICATION 
Region classification is a widely used method for extracting 
information on surface land cover from remotely sensed 
images. The resulting cartography is helping decision makers 
in different research fields. There exist a lot of image 
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