CHANGE DETECTION METHODOLOGY BASED ON
REGION CLASSIFICATION FUSION
Ting Liu, George Gigli, and George A. Lampropoulos
A.U.G. Signals, 73 Richmond St. West
Toronto, ON, Canada
Abstract - In this paper, several classification methods
are presented and a fusion structure is included to
improve the final classification performance. 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
Keywords: Distributed Processing, Change Detection, Fusion,
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
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 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
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. To improve
the overall performance, the decisions resulted from
different classification algorithms could be fused before
performing the change detection.
The remainder of the paper is organized as follows. A
detailed topographic change detection method based on
region classification is described in Section 2. Section 3
outlines the fusion structure. The definition of “layer” is
introduced in Section 4. Section 5 discusses the
distributed computing technique. Some simulations are
given in Section 6. In Section 7, the conclusions of the
paper are drawn.
2 Region Calssification
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 classification methods. The change
detection approach that will be proposed in section III is a
kind of post-classification method. In this paper, the
classification methods we used are: thresholding, fuzzy C-
means (FCM) and decision trees.
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