In this paper

some initial investigations are conducted to employ Clonal selection for classification of multi spectral remote sensing image. The clonal selection is used to explain the basic features of an adaptive immune response to an antigenic stimulus. The general algorithm

named clonal selection algorithm(CLONALG)

is derived from clonal selection to perform machine learning and pattern recognition tasks and it has been adopted to solve optimization problems. In this paper

image classification task by CLONALG is attempted and the preliminary results are provided. The experiment is consisted of two steps: Firstly

the classification task employs the property of clonal selection of immune system. The clonal selection proposes a description of the way that the immune systems copes with the pathogens to mount an adaptive immune response. Secondly

classification results are evaluated by applying three known algorithm: parallelepiped

minimum distance and maximum likelihood. It is demonstrated that our method is superior to the three traditional algorithms

and its overall accuracy and Kappa coefficient reach 93 63% and 0 915 respectively.