关键词: 遥感影像分类, 人工免疫, 独特型网络, 分区记忆模式

Abstract: Based on idiotypic immune network theory，a Regional-memory-pattern Artificial Idiotypic Network（RAIN） is proposed to classify multi-spectral remote sensing image.The immune memory antibodies of RAIN model are divided into two regions：specific memory antibody and free memory antibody.Specific memory antibody has several specific subregions sensitive to specific antigens，while free memory antibody region supplied kinds of specific memory antibodies for the former region.The adjustment and optimization of RAIN are realized according to antibody metdynamics.The initialization and training of RAIN are realized in the primary immune response process，and information extraction is executed in the second immune response process.At last，RAIN is used for the classification of ETM data.Accuracy and Kappa coefficient of our method are 92.6% and 0.91 respectively，while that of traditional Parallelepiped，Maximum Likelihood and Minimum Distance are 81.8%、82.2%、71.8%，and 0.78、0.78、0.65.The results show that RAIN is superior to three traditional classification algorithms.

Key words: remote sensing image classification, artificial immune, idiotypic network, regional-memory-pattern