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Article: An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery

TitleAn unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery
Authors
KeywordsArtificial immune system (AIS)
Clustering
Pattern recognition
Remote sensing
Unsupervised classification
Issue Date2006
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2006, v. 44, n. 2, p. 420-431 How to Cite?
AbstractA new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/330066
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhong, Yanfei-
dc.contributor.authorZhang, Liangpei-
dc.contributor.authorHuang, Bo-
dc.contributor.authorLi, Pingxiang-
dc.date.accessioned2023-08-09T03:37:32Z-
dc.date.available2023-08-09T03:37:32Z-
dc.date.issued2006-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2006, v. 44, n. 2, p. 420-431-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/330066-
dc.description.abstractA new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out. © 2006 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectArtificial immune system (AIS)-
dc.subjectClustering-
dc.subjectPattern recognition-
dc.subjectRemote sensing-
dc.subjectUnsupervised classification-
dc.titleAn unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2005.861548-
dc.identifier.scopuseid_2-s2.0-31444446724-
dc.identifier.volume44-
dc.identifier.issue2-
dc.identifier.spage420-
dc.identifier.epage431-
dc.identifier.isiWOS:000234902700017-

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