Automatic determination of categories in unsupervised classification
Sunpyo Hong, Kiyonari Fukue, Haruhisa Shimoda. Toshibumi Sakata
Tokai University Research and Information Center
2.28-4 Tomigaya, Shibuya – ku. Tokyon 151, Japan
Abstract
A cluster categorization method is necessary when an unsupervised classification is used for remote sensing image classification. It is desirable that this method is performed automatically, because manual categorization is a highly time consuming process.
In this paper, several automatic determination methods were proposed and evaluated. They ar4e 1) maximum number method. which assigns the target cluster to the category which occupies the largest area of that cluster: 2) maximum percentage method, which assigns the target cluster to the category which shows the maximum percentage within the category in that cluster : 3) minimum distance method, which assigns the target cluster to the category having minimum distance with that cluster. From the results of experiments, it was certified that the result by the minimum distance method was almost the same as the result made by a human operator.
Introduction
With the launch of second generation high resolution sensors like LANDSAT TM and SPOT HRV. clustering method has been revaluated recently. However, the main problem of clustering for practical use is that clustering is an unsupervised classification. That is , clusters generated by clustering are defined in feature vector space, not in image data. Therefore, in order to use that classified result for a; meaningful reference map, it is necessary to determine the relation of clusters and categories, and to label the classified result with the categories.
Conventionally, this relation have been determined mainly by interpretation of an operator. However, this process is time consuming and is not objective.
The purpose of this research is to try several methods of automatic cauterization and find and find out the most useful method. In this paper, 3 methods have been examined.
Problems of conventional Method
In this method, each classified cluster is overlaid with the target image data on the display, and that cluster is interpreted by an operator to determine the category, and that cluster is interpreted by that the obtained result is natural and reliable.
However, since everything is determined by an operator in this method, there many problems as follows.
- The result depends on the skill of an operator.
- Objective and quantitative evaluation is difficult.
- It is time consuming when the number of class is large or there are many small clusters.
Automatic Cluster Categorization Method
To solve the above problems, several automatic categorization methods are considered as follows. In all methods, training areas are first extracted from the image similar to supervised trainings.
1- Maximum Number Method
In this method, the number of pixels in each category for each cluster is calculated, Then the category having the maximum number is assigned to that cluster.
