ML Anthology

A New Visualization Paradigm for Multispectral Imagery and Data Fusion

Diego A. Socolinsky, Lawrence B. Wolff
CVPR 1999 pp. 1319-
doi:10.1109/CVPR.1999.786958 /cvpr/1999/socolinsky1999cvpr-new/

Abstract

We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed and a discussion is given as to why our algorithm is optimal and outperforms them. A variety of experimental results are presented.

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Cite

Text

Socolinsky and Wolff. "A New Visualization Paradigm for Multispectral Imagery and Data Fusion." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.786958

Markdown

[Socolinsky and Wolff. "A New Visualization Paradigm for Multispectral Imagery and Data Fusion." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/socolinsky1999cvpr-new/) doi:10.1109/CVPR.1999.786958

BibTeX

@inproceedings{socolinsky1999cvpr-new,
  title     = {{A New Visualization Paradigm for Multispectral Imagery and Data Fusion}},
  author    = {Socolinsky, Diego A. and Wolff, Lawrence B.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {1319-},
  doi       = {10.1109/CVPR.1999.786958},
  url       = {https://mlanthology.org/cvpr/1999/socolinsky1999cvpr-new/}
}