Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering

Aida Rodríguez*, Juan Luis Nieves, Eva Valero, Estíbaliz Garrote, Javier Hernández-Andrés, Javier Romero

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images. Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing the number of correctly classified pixels.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Image Processing
Subtitle of host publicationMachine Vision Applications V
DOIs
Publication statusPublished - 2012
EventImage Processing: Machine Vision Applications V - Burlingame, CA, United States
Duration: 25 Jan 201225 Jan 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8300
ISSN (Print)0277-786X

Conference

ConferenceImage Processing: Machine Vision Applications V
Country/TerritoryUnited States
CityBurlingame, CA
Period25/01/1225/01/12

Keywords

  • Fuzzy Clustering
  • Hyperspectral Imaging

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