Inspired by the idiotypic network theory, a new artificial immune network classifier for SAR image is proposed in this
paper. In the proposed algorithm, only one B-cell instead of many B-cells is used to denote a class so as to reduce the
scale of network as well as avoid the suppression operation between B-cells; moreover, a new affinity function based on
the correct rate is proposed to realize antigen priority based the evaluation strategy. The proposed algorithm has been
extensively compared with Fuzzy C-means (FCM), Multiple-Valued Immune Network algorithm (MVIN), and Clonal
Selection Algorithm for classifier (CSA) over two SAR images. The result of experiment indicates the superiority of the
algorithm over FCM, MVIN and CSA on classification accuracy and robustness.