Composite kernels conditional random fields for remote-sensing image classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Wu:2014:EL,
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author = "Junfeng Wu and Zhiguo Jiang and Jianwei Luo and
Haopeng Zhang",
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journal = "Electronics Letters",
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title = "Composite kernels conditional random fields for
remote-sensing image classification",
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year = "2014",
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volume = "50",
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number = "22",
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pages = "1589--1591",
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abstract = "The problem of classifying a remote-sensing image by
specifically labelling each pixel in the image is
addressed. A novel method, named composite kernels
conditional random field (CKCRF), which embeds multiple
kernels into a classical CRFs model is proposed. Rather
than manually selecting kernel-like KCRF, CKCRFs
chooses the appropriate kernel by training. Moreover, a
genetic programming-based decision-level fusion
framework is proposed to tackle the problem of feature
selection. It can select the appropriate features
suitable to each category. Evaluations show that CKCRFs
outperform CRFs and KCRFs, and CKCRFs with the fusion
scheme is better than that without the fusion step.",
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keywords = "genetic algorithms, genetic programming, geophysical
image processing, geophysical techniques, image
classification, image fusion, remote sensing, GP-based
decision-level fusion framework, composite kernels
conditional random fields, fusion scheme,
remote-sensing image classification",
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DOI = "doi:10.1049/el.2014.1964",
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ISSN = "0013-5194",
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notes = "Also known as \cite{6937260}",
- }
Genetic Programming entries for
Junfeng Wu
Zhiguo Jiang
Jianwei Luo
Haopeng Zhang
Citations