Low-level segmentation of multispectral images via agglomerative clustering of uniform neighbourhoods

摘要

A segmentation approach based on the concept of unsupervised classification of pixels is presented. Mean feature vectors of the classes are obtained from agglomerative-type clustering of the feature values computed over uniform neighbourhoods. Two assumptions are made in this development. The first is that at least one uniform neighbourhood can be found for each of the different categories present in the image. The second is that feature vectors of neighbourhoods representative of a particular category are similar to each other, but different from those of neighbourhoods belonging to other categories.The scheme has been applied to the segmentation of a three-band multispectral image of a terrain with satisfactory results. The method is computationally efficient, and requires minimal memory; hence it can be used in real time.