Classification of Hyperspectral Data Using a Multi-Channel Convolutional Neural Network
In recent years, deep learning is widely used for hyperspectral image (HSI) classification, among them, convolutional neural network (CNN) is most popular. In this paper, we propose a method for hyperspectral data classification by multi-channel convolutional neural network (MC-CNN). In this framework, one dimensional CNN (1D-CNN) is mainly used to extract the spectral feature of hyperspectral images, two dimension CNN (2D-CNN) is mainly used to extract the spatial feature of hyperspectral images, three-dimensional CNN (3D-CNN) is mainly used to extract part of the spatial and spectral information. And then these features are merged and pull into the full connection layer. At last, using neural network classifiers like logistic regression, we can eventually get class labels for each pixel. For comparison and validation, we compare the proposed MC-CNN algorithm with the other three deep learning algorithms. Experimental results show that our MC-CNN-based algorithm outperforms these state-of-the-art algorithms. Showcasing the MC-CNN framework has huge potential for accurate hyperspectral data classification.
KeywordsDeep learning Hyperspectral image classification Convolutional neural network Full connection layer Logistic regression
This work is supported by Anhui Provincial Natural Science Foundation (grant number 1608085MF 136), the National Science Foundation for China (Nos. 61602002 & 61572372).
- 6.Yu, D., Deng, L., Wang, S.: Learning in the deep-structured conditional random fields. In: NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, pp. 1848–1852 (2009)Google Scholar
- 7.Mohamed, A.R., Sainath, T.N., Dahl, G., Ramabhadran, B., Hinton, G.E., Picheny, M.A.: Deep belief networks using discriminative features for phone recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5060–5063 (2011)Google Scholar
- 8.Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 2012 (2013)Google Scholar
- 9.Schölkopf, B., Platt, J., Hofmann, T.: Greedy layer-wise training of deep networks. In: International Conference on Neural Information Processing Systems, pp. 153–160 (2007)Google Scholar
- 12.Geng, Y., Liang, R.Z., Li, W., Wang, J., Liang, G., Xu, C., Wang, J.Y.: Learning convolutional neural network to maximize Pos@Top performance measure (2016)Google Scholar
- 13.Geng, Y., et al.: A novel image tag completion method based on convolutional neural transformation. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10614, pp. 539–546. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68612-7_61CrossRefGoogle Scholar