Classification algorithms with hierarchical structure are quite effective for the classification of remotely sensed multi-spectral images because the data are vast in volume and have redundancy in spatial, spectral and/or, in some sense, temporal structure. The algorithms bring us high speed and high accuracy in the classification. A couple of hierarchical algorithms developed by the authors in spectral and spatial meaning are described with experimental results.

A new method is proposed for realizing a flexible change detection which is free from the limitation that multitemporal images must have the same spectral bands whose center wavelength and bandwidth are identical. As spaceborne multispectral scanners are continuously improved for performance and new scanners do not necessarily have the same spaectral bands for observation, this limitation is a serious obstacle for detecting long term temporal change. The proposed method removes this limitation by using an image normalization technique based on multiple regression analysis. The method is successfully applied to actual remotely sensed multitemporal images.