This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Hyperspectral image (HSI) classification is a hot topic in remote sensing community; many researchers have made a great deal of effort in this domain. Recently, deep learning-based manner paves a new way to better classification accuracy. However, the flow of information between layers and layers (e.g. max-pooling) in traditional deep architecture turns out to be ineffective. In this study, a novel spectral–spatial classification framework for HSI based on Capsule Network (CapsNet) and dynamic routing algorithm is introduced. The proposed architecture is composed of a hybrid of 1D and 2D convolutional layers and two capsule layers for better and effective mining and combining features. Consequently, experiments on two popular dataset indicate that CapsNet-based framework outperforms traditional CNN-based counterparts. Moreover, this study reveals great potential for CapsNet model in the field of HSI classification.
References
-
-
1)
-
3. Chang, C.-I.: ‘Hyperspectral data exploitation: theory and applications’ (Wiley–Interscience, New York, NY, USA, 2007).
-
2)
-
7. Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: ‘Classification of hyperspectral data from urban areas based on extended morphological profiles’, IEEE Trans. Geosci. Remote Sens., 2005, 43, (3), pp. 480–491.
-
3)
-
12. Chen, Y., Zhao, X., Jia, X.: ‘Spectral-spatial classification of hyperspectral data based on deep belief network’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2015, 8, (6), pp. 2381–2392.
-
4)
-
11. Chen, Y., Lin, Z., Zhao, X., et al: ‘Deep learning-based classification of hyperspectral data’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (6), pp. 2094–2107.
-
5)
-
10. He, Z., Wang, Q., Shen, Y., et al: ‘Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (8), pp. 5150–5163.
-
6)
-
19. Sabour, S., Frosst, N., Hinton, G.E.: ‘Dynamic routing between capsules’. Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 3857–3867.
-
7)
-
14. Romero, A., Gatta, C., Camps-Valls, G.: ‘Unsupervised deep feature extraction for remote sensing image classification’, IEEE Trans. Geosci. Remote Sens., 2016, 54, (3), pp. 1349–1362.
-
8)
-
6. Camps-Valls, G., Gomez-Chova, L., Munoz-Mari, J., et al: ‘Composite kernels for hyperspectral image classification’, IEEE Geosci. Remote Sens. Lett., 2006, 3, (1), pp. 93–97.
-
9)
-
16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Advances in Neural Information Processing Systems, Lake Tahoe, CA, USA, 2012, pp. 1097–1105.
-
10)
-
9. Fauvel, M., Benediktsson, J.A., Chanussot, J., et al: ‘Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles’, IEEE Trans. Geosci. Remote Sens., 2008, 46, (11), pp. 3804–3814.
-
11)
-
18. Glorot, X., Bengio, Y.: ‘Understanding the difficulty of training deep feedforward neural networks’. AISTATS, Sardinia, Italy, 2010.
-
12)
-
8. Quesada-Barriuso, P., Argüello, F., Heras, D.B.: ‘Spectral-spatial classification of hyperspectral images using wavelets and extended morphological profiles’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (4), pp. 1177–1185.
-
13)
-
2. Zheng, W., Hu, J., Zhang, W., et al: ‘Potential of geosynchronous SAR interferometric measurements in estimating three-dimensional surface displacements’, Sci. China, Inf. Sci., 2017, 60, p. 060304.
-
14)
-
13. Chen, Y., Jiang, H., Li, C., et al: ‘Deep feature extraction and classification of hyperspectral images based on convolutional neural networks’, IEEE Trans. Geosci. Remote Sens., 2016, 54, (10), pp. 6232–6251.
-
15)
-
4. Landgrebe, D.: ‘Hyperspectral image data analysis’, IEEE Signal Process. Mag., 2002, 19, (1), pp. 17–28.
-
16)
-
15. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, (7553), pp. 436–444.
-
17)
-
17. Ioffe, S., Szegedy, C.: ‘Batch normalization: accelerating deep network training by reducing internal covariate shift’. International Conference on Machine Learning, Lille, France, 2015.
-
18)
-
1. Guarnieri, A.M., Rocca, F.: ‘Options for continuous radar earth observations’, Sci. China, Inf. Sci., 2017, 60, p. 060301.
-
19)
-
5. Jia, X., Kuo, B.-C., Crawford, M.M.: ‘Feature mining for hyperspectral image classification’, Proc. IEEE, 2013, 101, (3), pp. 676–697.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2019.0526
Related content
content/journals/10.1049/joe.2019.0526
pub_keyword,iet_inspecKeyword,pub_concept
6
6