Clonal Selection Algorithm for Classification of Remote Sensing Imagery

A novel supervised classifier for remote sensing data by employing clonal selection algorithm(CSA)is presented for solving the problems,e.g.local optimum and robustness,in remote sensing imagery classification.In the classifier,band brightness is defined as antigen's attribute,and the image is classified into class with the maximum affinity by calculating the affinity between remote sensing pixel and antibody.Antibody's real-encoding mutation integrates affinity's ascending and system's variety.The concerned experiment shows that antibody's affinity increases along with its evolution,and CSA classification's overall accuracy is 92.9%.Comparing with conventional Maximum Likelihood,CSA can get better precision.