Labeling algorithm in unsupervised classification

Abstract

A categorization method for cluster is necessary when an unsupervised learning is used for remote sensing image data classification. It is desirable that this method is performed automatically, because manual categorization is a highly time consuming process. In this paper, several automatic categorization methods were proposed and evaluated. They are 1) maximum number method, which assigns the target cluster to the category that occupies the largest area of that cluster; 2) maximum occupation rate method, which assigns the target cluster to the category which shows the maximum occupation rate within the catetory in that cluster; 3) minimum distance method, which assigns the target cluster to the category having minimum distance with that cluster; 4) element ratio method, which assigns the target region to the category which has the most similar element ratio with that region, it was certified that the result by the minimum distance method was almost same as the result made by a human operator.