Article | The 29th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS), 2–3 June 2016, Malmö, Sweden | Non-Linear Hyperspectral Subspace Mapping using Stacked Auto-Encoder
Göm menyn

Title:
Non-Linear Hyperspectral Subspace Mapping using Stacked Auto-Encoder
Author:
Niclas Niclas: Swedish Defence Research Agency (FOI), Sweden David Gustafsson: Swedish Defence Research Agency (FOI), Sweden
Download:
Full text (pdf)
Year:
2016
Conference:
The 29th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS), 2–3 June 2016, Malmö, Sweden
Issue:
129
Article no.:
001
Pages:
10
No. of pages:
5–14
Publication type:
Abstract and Fulltext
Published:
2016-06-20
ISBN:
978-91-7685-720-5
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


Export in BibTex, RIS or text

Stacked Auto-Encoder (SAE) is a rather new machine learning approach which utilize unlabelled training data to learn a deep hierarchical representation of features. SAE:s can be used to learn a feature representation that preserve key information of the features, but has a lower dimensionality than the original feature space. The learnt representation is a non-linear transformation that maps the original features to a space of lower dimensionality. Hyperspectral data are high dimensional while the information conveyed by the data about the scene can be represented in a space of considerably lower dimensionality. Transformation of the hyperspectral data into a representation in a space of lower dimensionality which preserve the most important information is crucial in many applications. We show how unlabelled hyperspectral signatures can be used to train a SAE. The focus for analysis is what type of spectral information is preserved in the hierarchical SAE representation. Results from hyperspectral images of natural scenes with man-made objects placed in the scene is presented. Example of how SAE:s can be used for anomaly detection, detection of anomalous spectral signatures, is also presented.

Keywords: artificial intelligence

The 29th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS), 2–3 June 2016, Malmö, Sweden

Author:
Niclas Niclas, David Gustafsson
Title:
Non-Linear Hyperspectral Subspace Mapping using Stacked Auto-Encoder
References:

[1] Yoshua Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1):1–127, 2009.


[2] Y. Chen, X. Zhao, and X. Jia. Spectral - spatial classification of hyperspectral data based on deep belief network. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, PP(99):1–12, 2015.


[3] Yushi Chen, Zhouhan Lin, Xing Zhao, Gang Wang, and Yanfeng Gu. Deep learning-based classification of hyperspectral data. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 7(6):2094–2107, June 2014.


[4] M. D. Farrell and R. M. Mersereau. On the impact of pca dimension reduction for hyperspectral detection of dicult targets. IEEE Geoscience and Remote Sensing Letters, 2(2):192–195, April 2005.


[5] M. Fauvel, J. Chanussot, and J. A. Benediktsson. Kernel principal component analysis for feature reduction in hyperspectrale images analysis. In Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006, pages 238–241, June 2006.


[6] David Gustafsson, Henrik Petersson, and Mattias Enstedt. Deep learning: Concepts and selected applications. Technical Report FOID-0701-SE, Swedish Defence Research Agency (FOI), Sweden, December 2015.


[7] R. B. Palm. Prediction as a candidate for learning deep hierarchical models of data. Master’s thesis, Technical University of Denmark, DTU Informatics, 2012.


[8] A. Villa, J. Chanussot, C. Jutten, J. A. Benediktsson, and S. Moussaoui. On the use of ica for hyperspectral image analysis. In 2009 IEEE International Geoscience and Remote Sensing Symposium, volume 4, pages IV- 97-IV-100, July 2009.


[9] Xue wen Chen and Xiaotong Lin. Big data deep learning: Challenges and perspectives. Access, IEEE, 2:514–525, 2014.


[10] Huiwen Zeng and H.J. Trussell. Dimensionality reduction in hyperspectral image classification. In Image Processing, 2004. ICIP ’04. 2004 International Conference on, volume 2, pages 913–916 Vol.2, Oct 2004.

The 29th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS), 2–3 June 2016, Malmö, Sweden

Author:
Niclas Niclas, David Gustafsson
Title:
Non-Linear Hyperspectral Subspace Mapping using Stacked Auto-Encoder
Note: the following are taken directly from CrossRef
Citations:
No citations available at the moment


Responsible for this page: Peter Berkesand
Last updated: 2017-02-21