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ON A NEW CLASSIFICATION METHOD 
BY MULTI-DIMENSIONAL HISTOGRAM 
Y. Haba, T. Iizuka and N .Hakada 
Kanazawa Institute of Technology, P.0. Kanazawa-South, 
Nonoichimaci, Ogigaoka 7-1,Ishikawa 921, Japan 
ABSTRACT 
It has been generally accepted that the remotely sensed MSS 
data are the most efficient way immediately to obtain the 
information of a ground surface. They have universally been 
used as the economical means for environmental pollution 
monitoring, natural resource monitoring, land-use map, 
ecological map and so on. It is generally accepted that 
they should be basically classified prior to various kinds 
of their application. 
There is the unsupervised classification method which is 
superior to the supervised one from points of accuracy and 
universality. But in the conventional unsupervised 
method,there are such problems that the distribution of the 
clusters are assumed to be normal one and that it is time 
consuming to classify the MSS imagery due to the complicated 
computation proportional to the number of classes per pixel. 
Thus, a new classification method by using multi-dimensional 
histogram (MDH) table has been proposed. The MDH method has 
mainly three advantages. First,it has no constrains on the 
cluster distribution. Second, it is possible rapidly to 
classify the MSS cmagery owing to the table search method. 
Last.it requires the small capacity of the main storage for 
stowing the MDH table. As results, it has such more another 
advantage that a mini-computer is available for the 
unsupervised classification system which has been limitted 
only to a large scaled computer. 
INTRODUCTION 
Various approaches to the resource monitoring ,environmenta1 
pollution monitoring, ecological map, land-use map and so on 
have been reserched and developed by using the imageries 
remotely sensed by the space-boarn or air-boarn multi- 
spectral scanner(MSS) . It is necessarily important and 
inevitable that MSS data are classified by any means in 
advance of their application to the actual various problems. 
Classification of the MSS data should be analized not only 
by using the spector data per pixel but also by using the 
areal data as used in the texture analysis and the temporal 
analysis. Futhermore the high level pattern recognition 
technique like 'Production System' is desired to be applied 
to the classification of the MSS data. But it is also 
important to make the great efforts in order to obtain the