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Unsupervised Segmentation of Hyper-spectral Images via Diffusion Bases

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications

Authors: Alon Schclar 1 and Amir Averbuch 2

Affiliations: 1 Academic College of Tel-Aviv Yaffo, Israel ; 2 Tel Aviv University, Israel

Keyword(s): Segmentation, Diffusion Bases, Dimensionality Reduction, Hyper-spectral Sensing.

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 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-spect ral images obtained from an airborne hyper-spectral camera. (More)

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Paper citation in several formats:
Schclar, A. and Averbuch, A. (2017). Unsupervised Segmentation of Hyper-spectral Images via Diffusion Bases. In Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI; ISBN 978-989-758-274-5; ISSN 2184-3236, SciTePress, pages 305-312. DOI: 10.5220/0006503503050312

@conference{ijcci17,
author={Alon Schclar. and Amir Averbuch.},
title={Unsupervised Segmentation of Hyper-spectral Images via Diffusion Bases},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI},
year={2017},
pages={305-312},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006503503050312},
isbn={978-989-758-274-5},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI
TI - Unsupervised Segmentation of Hyper-spectral Images via Diffusion Bases
SN - 978-989-758-274-5
IS - 2184-3236
AU - Schclar, A.
AU - Averbuch, A.
PY - 2017
SP - 305
EP - 312
DO - 10.5220/0006503503050312
PB - SciTePress

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