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2020 | OriginalPaper | Buchkapitel

Hyperspectral Image Classification via Hierarchical Features Adaptive Fusion Network

verfasst von : Zehui Sun, Qin Xu, Fenglei Li, Yiming Mei, Bin Luo

Erschienen in: Advances in Brain Inspired Cognitive Systems

Verlag: Springer International Publishing

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Abstract

Recently, convolutional neural networks have attracted much attention due to its good performance in hyperspectral image classification. However, excessively increasing the depth of the network will lead to overfitting and vanishing gradient. Besides, previous networks rarely consider the related information among different convolution layers. In this paper we propose a hierarchical deep features adaptive fusion network (FAFNet) to address the above two problems. On the one hand, we use dense connectivity to overcome vanishing gradient and overfitting. On the other hand, we adaptively fuse different convolution layers by the learned weights which utilizing softmax to calculate. Experimental results on two well-known datasets demonstrate the excellent performance of the proposed method compared with other state-of-the-art methods.

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Ren, J., Zabalza, J., et al.: Effective feature extraction and data reduction with hyperspectral imaging in remote sensing. IEEE Sig. Process. Mag. 31(4), 149–154 (2014)CrossRef

Zabalza, J., Ren, J., et al.: Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185, 1–10 (2016)CrossRef

Bioucas-Dias, J.M., et al.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Rem. Sens. Mag. 1(2), 6–36 (2013)CrossRef

Li, W., Wu, G., Du, Q.: Transferred deep learning for anomaly detection in hyperspectral imagery. IEEE Geosci. Rem. Sens. Lett. 14(5), 597–601 (2017)CrossRef

Zhang, Y., Du, B., Zhang, L.: A sparse representation-based binary hypothesis model for target detection in hyperspectral images. IEEE Trans. Geosci. Rem. Sens. 53(3), 1346–1354 (2015)CrossRef

Zhang, L., Tao, D., Huang, X., Du, B.: Hyperspectral remote sensing image subpixel target detection based on supervised metric learning. IEEE Trans. Geosci. Rem. Sens. 52(8), 4955–4965 (2014)CrossRef

Blanzieri, E., Melgani, F.: Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Trans. Geosci. Rem. Sens. 46(6), 1804–1811 (2008)CrossRef

Ham, J., Chen, Y., et al.: Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. Rem. Sens. 43(3), 492–501 (2005)CrossRef

Cao, F., et al.: Linear vs nonlinear extreme learning machine for spectral-spatial classification of hyperspectral image. Sensors 17, 2603 (2017)CrossRef

10.

Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Rem. Sens. 42(8), 1778–1790 (2004)CrossRef

11.

Fang, L., et al.: Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Trans. Instrum. Measur. 66(7), 1646–1657 (2017)CrossRef

12.

Qiao, T., Yang, Z., et al.: Joint bilateral filtering and spectral similarity-based sparse representation: a generic framework for effective feature extraction and data classification in hyperspectral imaging. Pattern Recogn. 77, 316–328 (2017)CrossRef

13.

Chen, Y., et al.: Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Observ. Rem. Sens. 7(6), 2094–2107 (2014)

14.

Chen, Y., et al.: Spectral spatial classification of hyperspectral data based on deep belief network. IEEE J. Sel. Top. Appl. Earth Observ. Rem. Sens. 8(6), 1–12 (2015)

15.

Chen, Y., et al.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Rem. Sens. 54(10), 6262–6251 (2016)

16.

Huang, G., Liu, Z., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on CVPR, pp. 2261–2269 (2017)

17.

Sheng, W., et al.: Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification. arXiv:1905.06133 (2019)

18.

Li, X., et al.: Selective Kernel Networks. arXiv:1903.06586 (2019)

19.

Hu, W., et al.: Deep convolutional neural networks for hyperspectral image classification. J. Sens. 2, 1–12 (2015)CrossRef

20.

Li, W., Wu, G., et al.: Hyperspectral image classification using deep pixel-pair features. IEEE Trans. Geosci. Rem. Sens. 55(2), 844–853 (2016)CrossRef

21.

Mei, S., et al.: Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks. IEEE Trans. Geosci. Rem. Sens. 55(8), 4520–4533 (2017)CrossRef

22.

Fang, L., Liu, Z., Song, W.: Deep hashing neural networks for hyperspectral image feature extraction. IEEE Geosci. Rem. Sens. Lett., 1–5 (2019)

23.

Zhong, Z., Li, J., et al.: Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Trans. Geosci. Rem. Sens. 56, 847–858 (2018)CrossRef

24.

Waske, B., et al.: Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Trans. Geosci. Rem. Sens. 48(7), 2880–2889 (2010)CrossRef

25.

Zhang, H., Li, Y., Zhang, Y., Shen, Q.: Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network. Rem. Sens. 8, 438–447 (2017)

26.

Wang, L., Peng, J., Sun, W.: Spatial-spectral squeeze-and-excitation residual network for hyperspectral image classification. Rem. Sens. 11, 884 (2019)CrossRef

Titel: Hyperspectral Image Classification via Hierarchical Features Adaptive Fusion Network
verfasst von: Zehui Sun
Qin Xu
Fenglei Li
Yiming Mei
Bin Luo
Verlag: Springer International Publishing
Buch: Advances in Brain Inspired Cognitive Systems
Print ISBN: 978-3-030-39430-1

Electronic ISBN: 978-3-030-39431-8

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-39431-8_29

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