Two Sequential Stages Classifier for Multispectral Data

Mohamed, Refaat M.; Farag, Aly A.

doi:10.1109/CVPRW.2003.10059

Two Sequential Stages Classifier for Multispectral Data

Refaat M. Mohamed, Aly A. Farag

CVPRW 2003 pp. 67

doi:10.1109/CVPRW.2003.10059 /cvprw/2003/mohamed2003cvprw-two/

Abstract

In this paper, we present an approach for the classification of remote sensing multispectral data, which consists of two sequential stages. The first stage exploits the capabilities of the Support Vector Machines (SVM) approach for density estimation and uses it in a Bayes classification setup. In a typical image, the class of a pixel is highly dependent on the classes of its neighbor pixels. The second stage exploits the dependency of the classes. We incorporate this dependency using stochastic modeling of the context as a Markov Random Field (MRF). The MRF is modeled using Besag model and implemented using the Iterative Conditional Modes (ICM) algorithm. Results show that the stochastic modeling approach enhances the results and provides reasonable smoothness in the classified image.

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Cite

Text

Mohamed and Farag. "Two Sequential Stages Classifier for Multispectral Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003. doi:10.1109/CVPRW.2003.10059

Markdown

[Mohamed and Farag. "Two Sequential Stages Classifier for Multispectral Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003.](https://mlanthology.org/cvprw/2003/mohamed2003cvprw-two/) doi:10.1109/CVPRW.2003.10059

BibTeX

@inproceedings{mohamed2003cvprw-two,
  title     = {{Two Sequential Stages Classifier for Multispectral Data}},
  author    = {Mohamed, Refaat M. and Farag, Aly A.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2003},
  pages     = {67},
  doi       = {10.1109/CVPRW.2003.10059},
  url       = {https://mlanthology.org/cvprw/2003/mohamed2003cvprw-two/}
}