Abstract
In this paper, we conducted a study of several gradient descent methods namely gradient descent, stochastic gradient descent, momentum method, and AdaGrad for nonlinear mapping of hyperspectral satellite images. The studied methods are compared in terms of both data mapping error and operation time. Two possible applications of the studied methods are considered. First application is the nonlinear dimensionality reduction of the hyperspectral images for the further classification. Another application is the visualization of the hyperspectral images in false colors. The study was carried out using well known hyperspectral satellite images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
Hyperspectral Remote Sensing Scenes. http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes
Journaux, L., Foucherot, I., Gouton, P.: Nonlinear reduction of multispectral images by curvilinear component analysis: application and optimization. In: International Conference on CSIMTA 2004 (2004)
Kim, D.H., Finkel, L.H.: Hyperspectral image processing using locally linear embedding. In: First International IEEE EMBS Conference on Neural Engineering, pp. 316–319 (2003)
Lennon, M., Mercier, G., Mouchot, M., Hubert-Moy, L.: Curvilinear component analysis for nonlinear dimensionality reduction of hyperspectral images. Proc. SPIE 4541, 157–168 (2002)
Myasnikov, E.V.: Nonlinear mapping methods with adjustable computational complexity for hyperspectral image analysis. Proc. SPIE 9875, 987508-1–987508-6 (2015)
Rumelhart, D.E., Hintont, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Sammon Jr., J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. C–18(5), 401–409 (1969)
Shen-En, Q., Guangyi, C.: A new nonlinear dimensionality reduction method with application to hyperspectral image analysis. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 270–273 (2007)
Acknowledgments
This work was financially supported by Russian Foundation for Basic Research, projects no. \(15-07-01164-a\), \(16-37-00202\) mol_a.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Myasnikov, E. (2016). Evaluation of Stochastic Gradient Descent Methods for Nonlinear Mapping of Hyperspectral Data. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-41501-7_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41500-0
Online ISBN: 978-3-319-41501-7
eBook Packages: Computer ScienceComputer Science (R0)