Acta Optica Sinica, Volume. 33, Issue 11, 1128001(2013)

Improved Dimensionality Reduction Algorithm of Large-Scale Hyperspectral Scenes Using Manifold

Zhang Jingjing1,2、*, Zhou Xiaoyong1, and Liu Qi1
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  • 1[in Chinese]
  • 2[in Chinese]
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    References(24)

    [1] [1] Tong Qingxi, Zhang Bing, Zheng Lanfen. Hyperspectral Remote Sensing [M]. Beijing: China Higher Education Press, 2006.

    [2] [2] A N Gorban, Balazs Kegl, D C WunschII, et al.. Principal Manifolds for Data Visualization and Dimension Reduction [M]. Berlin: Springer-Verlag Berlin and Heideberg GmbH & Co. K, 2007.

    [3] [3] Du Peijun, Wang Xiaomei, Tan Kun, et al.. Dimensionality reduction and feature extraction from hyperspectral remote sensing imagery based on manifold learning [J]. Geomatics and Information Science of Wuhan University, 2011, 36(2): 148-152.

    [4] [4] J B Tenenbaum, V De Silva, J C Langford. A global geometric framework for nonlinear dimensionality reduction [J]. Science, 2000, 290(5500): 2319-2323.

    [5] [5] S T Roweis, L K Saul. Nonlinear dimensionality reduction by locally linear embedding [J]. Science, 2000, 290(5500): 2323-2326.

    [6] [6] Y Chen, M M Crawford, J Ghosh. Applying nonlinear manifold learning to hyperspectral data for land cover classification [C]. International Geoscience and Remote Sensing Symposium, Seoul, 2005, 6: 4311.

    [7] [7] D Guangjun, Z Yongsheng, J Song. Dimensionality reduction of hyperspectral data based on ISOMAP algorithm [C]. ICEMI′07.8th International Conference on Electronic Measurement and Instruments, IEEE, Xi′an,2007: 3-935-3-938.

    [8] [8] X R Wang, S Kumar, T Kaupp, et al.. Applying ISOMAP to the learning of hyperspectral image [C]. Proceedings of the 2005 Australasian Conference on Robotics & Automation. Australian Robotics & Automation Association, Sydney, 2005.

    [9] [9] C M Bachmann, T L Ainsworth, R A Fusina, et al.. Manifold coordinate representations of hyperspectral imagery: improvements in algorithm performance and computational efficiency [C]. Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International. IEEE, Honolulu, 2010: 4244-4247.

    [10] [10] C M Bachmann, T L Ainsworth, R A Fusina. Exploiting manifold geometry in hyperspectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 441-454.

    [11] [11] C M Bachmann, T L Ainsworth, R A Fusina. Improved manifold coordinate representations of large-scale hyperspectral scenes [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10): 2786-2803.

    [12] [12] M H C Law, A K Jain. Incremental nonlinear dimensionality reduction by manifold learning [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2006, 28(3): 377-391.

    [13] [13] H C Law. Clustering, Dimensionality Reduction, and Side Information [D]. Michigan: Michigan State University, 2006.

    [14] [14] D Zhao, L Yang. Incremental isometric embedding of high-dimensional data using connected neighborhood graphs [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2009, 31(1): 86-98.

    [15] [15] D K Agrafiotis, H Xu. A self-organizing principle for learning nonlinear manifolds [J]. Proceedings of the National Academy of Sciences, 2002, 99(25): 15869-15872.

    [16] [16] V A Tolpekin, A Stein. Quantification of the effects of land-cover-class spectral separability on the accuracy of Markov-random-field-based superresolution mapping [J]. Geoscience and Remote Sensing, IEEE Transactions on, 2009, 47(9): 3283-3297.

    [17] [17] T Murakami, S Ogawa, N Ishitsuka, et al.. Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains, Japan [J]. International Journal of Remote Sensing, 2001, 22(7): 1335-1348.

    [18] [18] S M Davis, D A Landgrebe, T L Phillips, et al.. Remote sensing: the quantitative approach [J]. New York: McGraw-Hill International Book Co., 1978. 405 p., 1978, 1.

    [19] [19] M L Davison. Multidimensional Scaling [M]. New York: Wiley, 1983.

    [20] [20] V Silva, J B Tenenbaum. Global versus local methods in nonlinear dimensionality reduction [J]. Advances in Neural Information Processing Systems, 2002, 15: 705-712.

    [21] [21] L K Saul, S T Roweis. Think globally, fit locally: unsupervised learning of low dimensional manifolds [J]. The Journal of Machine Learning Research, 2003, 4: 119-155.

    [22] [22] T Friedrich. Nonlinear dimensionality reduction-locally linear embedding versus isomap [C]. The University of Sheffield, 2004.

    [23] [23] M Bernstein, V De Silva, J C Langford, et al.. Graph Approximations to Geodesics on Embedded Manifolds [R]. Stanford: Stanford University, 2000.

    [24] [24] E Levina, P J Bickel. Maximum likelihood estimation of intrinsic dimension [J]. Ann Arbor MI, 2004, 48109: 1092.

    CLP Journals

    [1] Xinfang Xie, Xin Xu, Hao Dong, Han Wu, Luoru Li. A Semi-Supervised Dimension Reduction Method for Polarimetric SAR Image Classification[J]. Acta Optica Sinica, 2018, 38(4): 0428001

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    Zhang Jingjing, Zhou Xiaoyong, Liu Qi. Improved Dimensionality Reduction Algorithm of Large-Scale Hyperspectral Scenes Using Manifold[J]. Acta Optica Sinica, 2013, 33(11): 1128001

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: May. 8, 2013

    Accepted: --

    Published Online: Oct. 20, 2013

    The Author Email: Jingjing Zhang (helenzjj@aiofm.ac.cn)

    DOI:10.3788/aos201333.1128001

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