Abstract
The hyperspectral image classification method based on recurrent neural network (RNN) regards the spectral values of all bands of each pixel as spectral sequences. But a one-way RNN can only focus on current input and past memory states, not future memories. And RNN itself has the problem of severe gradient vanish. In this paper, bidirectional gated recurrent units (BiGRU) is used for the classification of hyperspectral images. Bi-directional can not only integrate past memory state and future memory state, but also solve the gradient punishment problem of RNN to a certain extent. And the proposed method obtains better classification performance.
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References
Tao C, Pan H, Li Y, Zou Z (2015) Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification. IEEE Geosci Remote Sens Lett 12(12):2438–2442
Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Selected Top Appl Earth Observ Remote Sens 8(6):2381–2392
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proc: NIPS, pp 1097–1105
Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251
Mou L, Ghamisi P, Zhu XX (2017) Deep recurrent neural networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(7):3639–3655
Acknowledgements
This work was supported by the National Key R&D Program of China (2016YFB0502502) and the National Natural Science Foundations of China (61871150).
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Liu, Y., He, H., Wang, X., Wang, Y., Chen, R. (2020). Hyperspectral Image Classification Based on Bidirectional Gated Recurrent Units. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_180
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DOI: https://doi.org/10.1007/978-981-13-9409-6_180
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