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Authors: Abir Zidi 1 ; Julien Marot 2 ; Klaus Spinnler 1 and Salah Bourennane 2

Affiliations: 1 Fraunhofer IIS, Germany ; 2 Institut Fresnel, France

ISBN: 978-989-758-089-5

Keyword(s): Non-negative Matrix Factorization, PARAFAC, Linear Algebra, Hyperspectral Image, Remote Sensing, Tensor.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors ; Multimodal and Multi-Sensor Models of Image Formation

Abstract: In the literature, there are several methods for multilinear source separation. We find the most popular ones such as nonnegative matrix factorization (NMF), canonical polyadic decomposition (PARAFAC). In this paper, we solved the problem of the hyperspectral imaging with NMF algorithm. We based on the physical property to improve and to relate the output endmembers spectra to the physical properties of the input data. To achieve this,we added a regularization which enforces the closeness of the output endmembers to automatically selected reference spectra. Afterwards we accounted for these reference spectra and their locations in the initialization matrices. To illustrate our methods, we used self-acquired hyperspectral images (HSIs). The first scene is compound of leaves at the macroscopic level. In a controlled environment, we extract the spectra of three pigments. The second scene is acquired from an airplane: We distinguish between vegetation, water, and soil.

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Paper citation in several formats:
Zidi, A.; Marot, J.; Spinnler, K. and Bourennane, S. (2015). Unmixing of Hyperspectral Images with Pure Prior Spectral Pixels.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 153-158. DOI: 10.5220/0005311101530158

@conference{visapp15,
author={Abir Zidi. and Julien Marot. and Klaus Spinnler. and Salah Bourennane.},
title={Unmixing of Hyperspectral Images with Pure Prior Spectral Pixels},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={153-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005311101530158},
isbn={978-989-758-089-5},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Unmixing of Hyperspectral Images with Pure Prior Spectral Pixels
SN - 978-989-758-089-5
AU - Zidi, A.
AU - Marot, J.
AU - Spinnler, K.
AU - Bourennane, S.
PY - 2015
SP - 153
EP - 158
DO - 10.5220/0005311101530158

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