Abstract
In this paper, present situation because of the high spectral image spectral information and spatial information leading to an increase in the increasing demand for new classification method of depth in recent years confidence in network feature extraction process large amounts of data, such as the Chinese information extraction, and other aspects of cancer determine the success of reality Combine. Introducing depth belief networks classify hyperspectral images, in excavating the hyperspectral data space information based on the study of the neighborhood mosaic spectral and spatial information used in combination, neighborhood stitching and spectral information integration and the weighted average of the empty Cape Joint strategy, through the experimental comparison with other methods to get the optimal weighted average method empty spectrum joint conclusions.
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References
Pu, R.L., Gong, P.: Hyperspectral remote sensing and Application. Higher Education Press, Beijing (2000)
Tong, Q.X., Zhang, B., Zheng, F.L.: Multi disciplinary applications of hyperspectral remote sensing. Electronic Industry Press, Beijing (2006)
Wang, J.Y., Xue, Y.Q., Shu, R., Yang, Y.D., Liu, Y.N.: Airborne Hyperspectral and Infrared Remote Sensing Technology and Application. In: Infrared Millimeter Waves and 14th International Conference on Teraherz Electronics, Joint International Conference on IRMMW-THzpp. 9, 18–22. Sept ( 2006)
Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.: Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43, 492–501 (2005)
Landgrebe, David, A.: Signal theory methods in multispectral remote sensing[M]. John, Wiley., Sons ( 2005)
Burges, C.J.C.: Dimension Reduction. M. Now Publishers Inc (2010)
Ratle, F., Camps-Valls, G., Weston, J.: Semisupervised neural networks for efficient hyperspectral image classification. J. IEEE Transactions on Geoscience and Remote Sensing. 48, 2271–2282 (2010)
Fauvel, M., Chanussot, J., Benediktsson, J.A.: Evaluation of kernels for multiclass classification of hyperspectral remote sensing data. In: Acoustics, Speech and Signal Processing. pp. II–II, IEEE (2006)
Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation . J. Geoscience and Remote Sensing. 49, 3973–3985 (2011)
Gu, Y., Wang, C., You, D., Zhang, Y., Wang, S., Zhang, Y.: Representative multiple kernel learning for classification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 50, 2852–2865 (2012)
Zhang, X.G.: On the statistical learning theory and SVM. J. Automation Journal. 26, 32 (2000)
Sun, J.G.: Application of deep confidence network in spam filtering. J. Computer application. 34, 1122–1125 (2014)
He, Z.B.: Singer identification based on convolutional depth confidence network [D].Guangzhou: South China University of Technology (2015)
Furao, Shen.: Forecasting exchange rate using deep belief networks and conjugate gradient method, Neurocomputing Volume 167, 1, pp. 243–253. November (2015)
Chen, Y.: Chinese information extraction method based on depth confidence network[D]. Harbin: Harbin Institute of Technology (2014)
Lin, H.Z.: Research on feature extraction and classification of hyperspectral images based on automatic coding machine. D. Harbin: Harbin Institute of Technology (2014)
Zhao, X.: Classification method of hyperspectral data based on integrated depth confidence network [D]. Harbin: Harbin Institute of Technology (2015)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Computation. 14, 1771–1800 (2002)
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Lin, L., Dong, H., Song, X. (2017). DBN-based Classification of Spatial-spectral Hyperspectral Data. In: Pan, JS., Tsai, PW., Huang, HC. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-319-50212-0_7
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DOI: https://doi.org/10.1007/978-3-319-50212-0_7
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