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- W2384804975 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." @default.
- W2384804975 created "2016-06-24" @default.
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- W2384804975 date "2008-01-01" @default.
- W2384804975 modified "2023-09-26" @default.
- W2384804975 title "Classification of remote sensing image based on immune network" @default.
- W2384804975 hasPublicationYear "2008" @default.
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