Unsupervised segmentation of hyper-spectral images via diffusion bases

Alon Schclar, Amir Averbuch

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

Abstract

In the field of hyper-spectral sensing, sensors capture images at hundreds and even thousands of wavelengths. These hyper-spectral images, which are composed of hyper-pixels, offer extensive intensity information which can be utilized to obtain segmentation results which are superior to those that are obtained using RGB images. However, straightforward application of segmentation is impractical due to the large number of wavelength images, noisy wavelengths and inter-wavelength correlations. Accordingly, in order to efficiently segment the image, each pixel needs to be represented by a small number of features which capture the structure of the image. In this paper we propose the diffusion bases dimensionality reduction algorithm (Schclar and Averbuch, 2015) to derive the features which are needed for the segmentation. We also propose a simple algorithm for the segmentation of the dimensionality reduced image. We demonstrate the proposed framework when applied to hyper-spectral microscopic images and hyper-spectral images obtained from an airborne hyper-spectral camera.

Original languageEnglish
Title of host publicationIJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence
EditorsChristophe Sabourin, Juan Julian Merelo, Una-May O'Reilly, Kurosh Madani, Kevin Warwick
PublisherSciTePress
Pages305-312
Number of pages8
ISBN (Print)9789897582745
DOIs
StatePublished - 2017
Event9th International Joint Conference on Computational Intelligence, IJCCI 2017 - Funchal, Madeira, Portugal
Duration: 1 Nov 20173 Nov 2017

Publication series

NameIJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence

Conference

Conference9th International Joint Conference on Computational Intelligence, IJCCI 2017
Country/TerritoryPortugal
CityFunchal, Madeira
Period1/11/173/11/17

Keywords

  • Diffusion Bases
  • Dimensionality Reduction
  • Hyper-spectral Sensing
  • Segmentation

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