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Research on Image Classification Algorithm Based on Artificial Immune Learning

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Abstract

On the basis of analyzing immune learning mechanism, by modeling for image classification, we can solve the problem of remote sensing image classification by using the basic principles of the use of immune learning. We have realized a classification algorithm with a function of the immune learning. Classification algorithm divides each major category into a number of small categories and the antigen population evolutionary process of each category is considered separately, therefore the convergence time is greatly decreased. When classifying, we use a variety of different ways to discriminate and introduce artificial priori knowledge to improve the classification accuracy. The results show that the algorithm can be well applied in remote sensing image classification.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong Province, P.R. China, 271018

    Chengming Zhang, Yong Liang, ShuJing Wan & Jinping Sun

  2. Taian tongli Computer Software Co., Ltd., Taian, Shandong Province, P.R. China, 271018

    Dalei Zhang

Authors
  1. Chengming Zhang

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  2. Yong Liang

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  3. ShuJing Wan

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  4. Jinping Sun

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  5. Dalei Zhang

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Editor information

Editors and Affiliations

  1. EU-China Center for Information & Communication Technologies (CICTA), China Agricultural University, 17 Tsinghua East Road, 100083, Beijing, P.R. China

    Daoliang Li  & Yingyi Chen  & 

  2. College of Mechanical and Electronic Engineering, East China Jiaotong University, Shuanggang Road, 330013, Nanchang, Jiangxi, China

    Yande Liu

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© 2011 IFIP International Federation for Information Processing

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Zhang, C., Liang, Y., Wan, S., Sun, J., Zhang, D. (2011). Research on Image Classification Algorithm Based on Artificial Immune Learning. In: Li, D., Liu, Y., Chen, Y. (eds) Computer and Computing Technologies in Agriculture IV. CCTA 2010. IFIP Advances in Information and Communication Technology, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18354-6_48

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