A binary division algorithm for clustering remotely sensed multi-spectral images

A new method is proposed for clustering remotely sensed multi-spectral images with both high accuracy and high efficiency. For high speed processing, we project image data onto one dimensional sub-space, and limit the number of boundaries in the sub-space. The optimal sub-space and boundary are selected so that the ratio of the variance of within distance to the variance of between distance takes the minimum value. Image data are repeatedly divided into two groups until all of the groups consist of a single cluster. Performance of the proposed method was better than that of ISODATA in both speed and accuracy. The method was successfully applied to actual remotely sensed multi-spectral images.<>