Efficient multi-stage system for unsupervised classification and its application of KOMPSAT-I imagery

Proposes an image processing system of unsupervised analysis. This system integrates low-level segmentation and high-level classification. The segmentation and classification are conducted respectively with and without spatial constraints on merging by a hierarchical clustering procedure. The clustering utilizes mutually closest neighbors to find the best pair to be merged. To provide the improved contents for the segmentation, an adaptive smoothing approach has been employed to reduce random noise in the original data. The proposed system has been applied to images of the Korean Peninsula collected on KOMPSAT-I.