A synergistic automatic clustering technique (SYNERACT) for multispectral image Analysis

This study aimed to develop a synergistic automatic clustering technique (SYNERACT) that combined the hierarchical descending and ISODATA clustering procedures to avoid those limitations and could serve as an alternative to ISodATA for multispectral image analysis.

Abstract

The Iterative Self-organizing Data Analysis Technique (ISODATA) has been widely used in unsupervised and supervised classification. However, ISODATA suffers from several limitations. The user often spends much analyst time on specifying input parameters by trial and error, particularly initial cluster centers. Of more importance, an inappropriate choice of initial clusters may cause poor classification results. ISODATA is computationally intensive because of its itemtive process. This study aimed to develop a synergistic automatic clustering technique (SYNERACT) that combined the hierarchical descending and ISODATA clustering procedures to avoid those limitations. The two methods were compared using multispectml digitized video images. An inappropriate choice of initial seeds for ISODATA was shown to reduce accuracies significantly In contrast, SYNERACT was capable of determining the suitable locations for the initial clusters automatically from the data, thereby avoiding those limitations. Owing to this capability, SYNERACT was not so heavily dependent on the itemtive process as was ISODATA, and thus was much faster than ISODATA. SYNERACT also matched ISODATA in accuracy, Accordingly, SYNERACT could serve as an alternative to ISODATA for multispectral image analysis. lntroductlon Clustering used for unsupervised classification is one of the most often used methods for extracting information from remotely sensed data of the Earth (Jensen, 1996). Clustering can also be used to determine the natural spectral groupings present in a data set. Thus, some of the unique classes, but with very