clustering methods unsupervised classification
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2008-03-15
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Rasim Latifovic, Josef Cihlar, Jean Beaubien
Intermap Technologies Ltd., Ottawa, Canada
Canada Centre for Remote Sensing, Ottawa, Canada
Canadian Forest Service, Canada
ABSTRACT
In this paper, we have examined characteristics of spectral clusters produced by several
unsupervised classification algorithms. We have also designed a new cluster merging
strategy for a previously developed unsupervised classification procedure. The clustering
methods were compared using a summer Landsat Thematic Mapper image from central
Canada which contained forest, cropland, wetland and other cover types. We have found
that a strategy employing spectral similarity and cluster size to guide the cluster merging
process yields clusters in which spectral homogeneity and size are well balanced across the
range of spectral clusters found in the scene. The new clustering approach requires only
three control parameters, thus facilitating consistent results when applied in other areas. The
results also suggest that the dual cluster size – spectral homogeneity approach produces
more consistent and refined clustering results than purely statistically-based methods,
although testing over a broader range of conditions is needed to ascertain this with
confidence.
1.0 INTRODUCTION
Image classification is an important analysis method for extracting information from multispectral
remotely sensed images. A major application is in land cover mapping, where the goal is to obtain
relatively few classes (~10-30). However, a large number of combinations of spectral values from
individual bands is typically found in such images. The aim of classification techniques is thus to reduce
the number of individual combination to a small number of classes. Among the various classification
approaches, unsupervised image classification methods are designed to make the best possible use of the
overall spectral content of an image, with the important condition that these remain as thematically uniform
as possible. However, thematic content cannot be evaluated until the number of clusters is relatively small
and can be grouped into the desired classification legend. Thus, during the clustering process the
homogeneity of clusters is typically evaluated using spectral measures.
In the development of an unsupervised classification method, two problems must be solved: a way
for locating centroids of the initial clusters in the multispectral space, and the process of finding the
optimum location of the final clusters. Various algorithms have been developed to deal with these steps.
For example, the K-means algorithm (Tou and Gonzales, 1974) selects the desired number of cluster
centroids in some manner and then attempts to find their optimum location by moving these around in
subsequent iterations. The location of the new centroids at each iteration is updated based on the mean
spectral characteristics of the pixels currently in the cluster. ISODATA (Tou and Gonzales, 1974) uses a
more complex control of the process leading to new cluster locations; at each iteration, clusters are split or
lumped depending on the spectral cohesiveness within clusters in relation to that among clusters. In their
Classification by Progressive Generalization (CPG) method, Cihlar et al. (1998) assumed that all initial
∗
Presented at the Fourth International Airborne Remote Sensing Conference and Exhibition/ 21
st
Canadian
Symposium on Remote Sensing , Ottawa, Ontario, Canada, 21-24 June 1999.
CLUSTERING METHODS FOR UNSUPERVISED CLASSIFICATION
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