Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science

Anomaly Detection Combined with Spectral Function Analysis in Hyperspectral Imagery

Authors
Xiaohan Zhang, Guang Yang, Yongbo Yang, Junhua Huang
Corresponding Author
Xiaohan Zhang
Available Online June 2016.
DOI
10.2991/icamcs-16.2016.10How to use a DOI?
Keywords
Hyperspectral Imagery, Anomaly Detection, Spectral Function, Area under Spectral Profile
Abstract

To improve the time performance of hyperspectral imagery anomaly detection, this paper proposes the conception of spectral function. Using the features of spectral function such as the area under spectral profile (AUSP), potential target pixels can be separated from background pixels. Then use the data of background area to carry out anomaly detection. In this way can we save time, increase algorithm running speed, and increase the performance of original detection algorithm. At last experiment is carried to verifying the efficiency of the method proposed in this paper.

Copyright
© 2016, 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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Volume Title
Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
Series
Advances in Engineering Research
Publication Date
June 2016
ISBN
978-94-6252-189-6
ISSN
2352-5401
DOI
10.2991/icamcs-16.2016.10How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Xiaohan Zhang
AU  - Guang Yang
AU  - Yongbo Yang
AU  - Junhua Huang
PY  - 2016/06
DA  - 2016/06
TI  - Anomaly Detection Combined with Spectral Function Analysis in Hyperspectral Imagery
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 42
EP  - 46
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.10
DO  - 10.2991/icamcs-16.2016.10
ID  - Zhang2016/06
ER  -