Comparison of Several Hyperspectral Image Fusion Methods for Visualization

DOI

10.2991/ameii-15.2015.119How to use a DOI?

Keywords

hyperspectral image; visualisation; colour representation; comparison

Abstract

Hyperspectral image visualization is an important research aspect in hyperspectral image fusion. This paper compared four typically used hyperspectral image visualization methods: method based on bilateral filter, method based on Principal Component Analysis (PCA), method based on independent component analysis (ICA) and method based on optimization. Fusion framework and scheme are explained briefly. Two sets of images obtained by AVIRIS and ROSIS sensors are used in our experiments, and four statistical assessment parameters, namely variance, entropy, average gradient and fusion factor are adopted to comparatively analyze the fusion results. The comparison results show that the effects of bilateral filter method, PCA method and optimization method are similar, and they are superior to ICA method.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hongwen Lin
AU  - Anqing Zhang
AU  - Shaoqing Yang
PY  - 2015/04
DA  - 2015/04
TI  - Comparison of Several Hyperspectral Image Fusion Methods for Visualization
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 646
EP  - 651
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.119
DO  - 10.2991/ameii-15.2015.119
ID  - Lin2015/04
ER  -