Composite kernels conditional random fields for remote-sensing image classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Junfeng Wu and Zhiguo Jiang and Jianwei Luo and 
                 Haopeng Zhang",
  journal =      "Electronics Letters",
  title =        "Composite kernels conditional random fields for
                 remote-sensing image classification",
  year =         "2014",
  volume =       "50",
  number =       "22",
  pages =        "1589--1591",
  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.",
  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",
  DOI =          "doi:10.1049/el.2014.1964",
  ISSN =         "0013-5194",
  notes =        "Also known as \cite{6937260}",

Genetic Programming entries for Junfeng Wu Zhiguo Jiang Jianwei Luo Haopeng Zhang