Unsupervised Multispectral Image Classification using Artificial Ants

Based on the existing works dealing on data clustering with artificial ants, we contribute in this paper to resolve a real clustering problem related on unsupervised multispectral image classification using ants approach, where classes are found without the a priori knowledge of the correct number of classes. Knowing that most of the unsupervised classification methods require the definition of a probable number of classes and an initial partition, the proposed ant-based approach is very interesting insofar for remotely sensed data over the whole of earth, it is not easy to obtain this a priori knowledge.