Supervised and unsupervised classification by histogram overlay techniques

Abstract

A new classification technique was proposed from the viewpoint of memory saving, as well as intensive processing time reduction, to meet the strong requirement for easier operation on a personal computer. To carry out this process efficiently, some neighbouring pixels were lumped into a cell. This idea is based on the fact that changes of the CCT counts in the sea area are monotone, that is histograms are symmetrical, and that cell by cell classification is, therefore, sufficient.

First, a cell distance was denned by extending the concept of the Mahalano-bis' distance, which is the statistical difference between a cell and a cluster. The classification results agree well with those of the conventional Maximum Likelihood Method. We define this method as CDM (Cell Distance Method).

Secondly, an alternative concept which indicates the degree of similarity between two cells was proposed. It was found that this concept, defined as HOM (Histogram Overlay Method), not only improves the speed of processing image data but also has a close relation with the cell distance. In fact, it corresponds fairly to the cell distance under a certain condition.

Thirdly, these two methods were extended to unsupervised classification and applied to the investigation of turbidity in the sea around Hiroshima and Kure, West Japan.