In this paper

some initial investigations are conducted to employ Artificial immune system（AIS） for classification of remotely sensed images. As a novel branch of computational intelligence

AIS has strong capabilities of pattern recognition

learning and associative memory

hence it is natural to view AIS as a powerful information processing and problem-solving paradigm in both the scientific and engineering fields. Artificial immune system posses nonlinear classification properties along with the biological properties such as self/nonself identification

positive and negative selection

clonal selection. Therefore

AIS

like genetic algorithms and neural nets

is a tool for adaptive pattern recognition. However

few papers have concerned applications of AIS in feature extraction/classification of aerial or high resolution satellite image and how to apply it to remote sensing imagery classification is very difficult because of its characteristics of huge volume data. Remote sensing imagery classification task by Artificial immune system 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 the immune systems copes with the pathogens to mount an adaptive immune response. Secondly

classification results are evaluated by three known algorithms: 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 89.80% and 0.8725 respectively.