Nonlinear Feature Extraction for Hyperspectral Images
DOI:
https://doi.org/10.18100/ijamec.74610Keywords:
feature extraction, spatial dependency, dimension reduction, manifold learning.Abstract
In this study non-linear dimension reduction methods have been applied to a hyperspectral image in order to increase the classification accuracy in feature extraction step. Furthermore, image segmentation has been ensured the by taking into consideration the spatial synthesis of hyperspectral images and passing from high-dimensional space to low dimensional space. It has been compared the results obtained from the image segmentation made by taking one pixel from this spatial synthesis. The advantages of the effects of the results of the dimension reduction techniques made by facing neighbor pixels on the segmentation of hyper-spectral image have been displayed in the experimental results part.Downloads
References
A. Mohan, G. Sapiro and E. Bosch, “Spatially coherent Nonlinear Dimensionality Reduction and. Segmentation of Hyperspectral Images,” IEEE Geosci.and Remote Sens., vol. 4, no.2, pp.206-210, April 2007.
L. Macter, E. Postma, and J. Heik, "Dimensionality reduction:A comparative review", Ticc Tilburg University, October 26, 2009.
E. Namey, G. Guest, L. Thairy and L. Johnson,“Data reduction techniques for large qualitative data sets”, Handbook for team-based qualitative research, pp.137-162, March 29, 2007.
Y. Hou, P. Zhang, X. Xu, X. Zhang and W. Li,“Nonlinear dimensionality reduction by Locally Linear Inlaying,” IEEE Transactions of Neural Networks, vol.20, no.2, pp.300-315, February 2009.
W. Kim, M. Crawford and S. Lee, “Integrating spatial proximity with manifold learning for hyperspectral data," Korean Journal of Remoting Sensing, vol.26, no.6, pp.693-703, 2010.
Melgani F, M. Bruzone L , “Classification of hyperspectral remote sensing images with support vector machines," IEEE Transactions Geoscieence and Remote Sensing on volume 42, Issue 8, pp 1778-1790, Agust 2004.
X. Huang, L. Zhang, “A comparative study of spatial approaches for urban mapping using hyperspectral Rosis images over Pavia City,"volume 30, Issue 12, 2009.
M. Fong, “ Dimension Reduction on Hyperspectral Images”, Project Report, REU projects, UCLA Dept of ath. Agu 2007.
Melba M. Crawford, Li Ma and W. Kim, “Exploring nonlinear manifold learning for classification of hyperspectral data,” Augmented vision and reality volum 3, pp 207-234, 2011.
Kim W, “Multiresolution manifold learning for classification of hyperspectral data,” IEEE International Geoscience and Remote Sensing Symposium, pp.3785-3788, July 2007.
M. Fauvel, J. Benediksson, J. Chanussot and Johannes R.Sveinsson, “Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles,” IEEE Transactions on Geoscience and Remote Sensing Society,vol.46, no.11, pp.30804-3814, Nov 2008.
Y Chen, M.Crawford and J. Ghost, “Applying nonlinear manifold learning to hyperspectral data for land cover classification,” in Processing of the IEEE International Geoscience and Remote Sensing Symposium, vol.6, pp.4311-4314, 2005.
S.T.Roweis and L.K.Saul, “Nonlinear dimensionality reduction by Local Linear Embedding,” Science, vol.290, no.5500, pp.2323- 2326, 2000.
Shen-En Qian, Chen G, “A new nonlinear dimensionality reduction method with application to hyperspectral image analysis,” IEEE International Geoscience and Remote Sensing Symposium, pp.270-273, July 2007.
Charles M.Banchmann, Thomas L.Ainsworth, Robert A.Fusina, “Exploiting manifold geometry in hyperspectral images,” IEEE Transactions on volume 43,no 3, pp.441-454, March 2005.
Bo Du, L. Zhang, L. Zhang, T. Chen and Ke Wu, “A discriminative manifold learning based dimension reduction method for hyperspectral classification,” International Journal of Fuzzy Systems, vol.14, no.2, June 2012.
Costa J.A, Hero A.O,“Classification constrained dimensionality reduction,” IEEE International Conference on Speech and Signal Processing, vol 5, pp. 1077-1080, March 2005.
Wang X.R, Ramos F, Kaupp T,“Probabilistic classification of hyperspectral images by learning nonlinear dimensionality reduction mapping,” International Conference on Information Fusion, July 2006.
L. O. Jimenez, and D. A. Landgrebe, "Supervised Classification in High Dimensional Space: Geometrical, Statistical, and Asymptotical Properties of Multivariable Data," IEEE Transactions on Systems, Man and Cybernetics, vol 28, no. 1, pp. 39-54, Feb. 1998.
J.B. Tenenbaum, V. de Silva, and J.C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, pp. 2319-2323, Dec 2000.
Downloads
Published
Issue
Section
License
Copyright (c) 2015 International Journal of Applied Methods in Electronics and Computers
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
