Abstract
Currently, the dimensionality of hyperspectral images is increasing, and the images have the characteristics of nonlinearity and spatial correlation, making it more and more difficult to classify these data. In this study, convolutional neural network (CNN) which has been successfully applied in image recognition and language detection is introduced. The spectral and spatial information is combined and used for hyperspectral image classification. According to the character of CNN that its input is two-dimensional image data, two methods are proposed converting the spectral and spatial information of hyperspectral images into two dimensional images. One of them converts the spatial-spectral information into gray level images and uses the varying texture features between spectral bands. The other converts the spatial-spectral information into waveforms and uses the wave characteristics of the spectral bands. Experiments on KSC and Pavia U data sets demonstrate the feasibility and efficacy of CNN in hyperspectral image classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bachmann, C.M., Ainsworth, T.L.,Fusina, R.A.: Improved manifold coordinate representations of large-scale hyperspectral scenes. J. Geo. and Remo. Sens. 44, 2786–2803 (2006).
Mura, M.D., Villa, A., Benediktsson, J.A., Chanussot, J.,Bruzzone, L.: Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. J. Geos. and Remo. Sens. Lett. 8, 542–546 (2011)
Mianji, F.A., Zhang, Y.: Robust hyperspectral classification using relevance vector machine. J. Geos. and Remo. Sens. 49, 2100–2112 (2011)
Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. J. Patt. Reco. 43, 2367–2379 (2010)
Tarabalka, Y., Benediktsson, J.A., Chanussot, J.: SpectralCspatial classification of hyperspectral imagery based on partitional clustering techniques. J. IEEE TGRS. 47, 2973–2987 (2009)
Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. J. IEEE TIP. 22, 1430–1443 (2013)
Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Multiple spectralCspatial classification approach for hyperspectral data. J. IEEE TGRS.48, 4122–4132 (2010)
Tarabalka, Y., Fauvel, M., Chanussot, J., Benediktsson, J.A.: SVM-and MRF-based method for accurate classification of hyperspectral images. J. IEEE GRSL. 7, 736–740 (2010)
Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. J. IEEE TGRS. 49, 3973–3985 (2011)
Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification via kernel sparse representation. J. IEEE TGRS. 51, 217–231 (2013)
Fang, L., Li, S., Kang, X., Benediktsson, J.A.: SpectralCspatial hyperspectral image classification via multiscale adaptive sparse representation. J. IEEE TGRS. 52, 7738–7749 (2014)
Zhang, H., Li, J., Huang, Y., Zhang, L.: A nonlocal weighted joint sparse representation classification method for hyperspectral imagery. J. IEEE JSTAEORS. 7, 2056–2065 (2014)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. J. Science. 313, 504–507 (2006)
Dahl, G.E., Dong, Y., Li, D., Acero, A.: Large vocabulary continuous speech recognition with context-dependent DBN-HMMS. In: IEEE ICASSP, pp. 4688–4691. IEEE (2011)
Mohamed, A., Sainath, T.N., Dahl, G., Ramabhadran, B.: Deep belief networks using discriminative features for phone recognition. In: ICASSP, pp. 5060–5063. IEEE (2011)
Nair, V., Hinton, G.E.: 3D object recognition with deep belief nets. In: ANIPS, pp. 1339–1347 (2009)
Fasel, I., Berry, J., Deep belief networks for real-time extraction of tongue contours from ultrasound during speech. In: 20th ICPR, pp. 1493–1496. IEEE (2010)
Deselaers, T., Hasan, S., Bender, O., Ney, H.: A deep learning approach to machine transliteration. In: PFWSMT, pp. 233–241 (2009)
Li, D., Seltzer, M.L., Dong, Y., Acero, A., Mohamed, A.R., Hinton, G.E.: Binary coding of speech spectrograms using a deep auto-encoder In: Interspeech, pp. 1692–1695 (2010)
LeCun, Y., Boser, B., Denker, J.S., Henderson, D.: Backpropagation applied to handwritten zip code recognition. J. Neural Comp. 1, 541–551 (1989)
Rosenblatt, F.: The perceptrona probabilistic model for information storage and organization in the brain. J. Psyc. Rev. 65, 386 (1958)
Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y.: Deep learning-based classification of hyperspectral data. J. Sel. Top. in Appl. Earth Observ. and Remo. Sens. 7, 2094–2107(2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Lin, L., Song, X. (2017). Using CNN to Classify Hyperspectral Data Based on Spatial-spectral Information. 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_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-50212-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50211-3
Online ISBN: 978-3-319-50212-0
eBook Packages: EngineeringEngineering (R0)