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EZ | 在遥感场景分类中使用的暹罗卷积神经网络 | 04

2019-07-16  本文已影响0人  杜若飞er

        引用

[1] A. Oliva and A. Torralba, Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. Norwell, MA, USA: Kluwer, 2001.

[2] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004.

[3] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp. 886–893.

[4] D. Lin, K. Fu, Y. Wang, G. Xu, and X. Sun, “MARTA GANs: Unsuper- vised representation learning for remote sensing image classification,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 11, pp. 2092–2096, Nov. 2017.

[5] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006.

[6] G. J. Scott, M. R. England, W. A. Starms, R. A. Marcum, and C. H. Davis, “Training deep convolutional neural networks for land– cover classification of high-resolution imagery,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 4, pp. 549–553, Apr. 2017.

[7] Y. Jia et al. (2014). “Caffe: Convolutional architecture for fast feature embedding.” [Online]. Available: https://arxiv.org/abs/1408.5093

[8] C. Szegedy et al., “Going deeper with convolutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. CVPR, Jun. 2015, pp. 1–9.

[9] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 770–778.

[10] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Int. Conf. Neural Inf. Process. Syst., 2012, pp. 1097–1105.

[11] G. Cheng, Z. Li, X. Yao, L. Guo, and Z. Wei, “Remote sensing image scene classification using bag of convolutional features,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 10, pp. 1735–1739, Oct. 2017.

[12] K. Simonyan and A. Zisserman. (2014). “Very deep convolutional networks for large-scale image recognition.” [Online]. Available: https://arxiv.org/abs/1409.1556

[13] S. Chaib, H. Liu, Y. Gu, and H. Yao, “Deep feature fusion for VHR remote sensing scene classification,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 8, pp. 4775–4784, Aug. 2017.

[14] E. Li, J. Xia, P. Du, C. Lin, and A. Samat, “Integrating multilayer features of convolutional neural networks for remote sensing scene classification,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 10, pp. 5653–5665, Oct. 2017.

[15] Z. Ren, B. Hou, Z. Wen, and L. Jiao, “Patch-sorted deep feature learning for high resolution SAR image classification,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 11, no. 9, pp. 3113–3126, Sep. 2018.

[16] B. Hou, Z. Wen, L. Jiao, and Q. Wu, “Target-oriented high-resolution SAR image formation via semantic information guided regularizations,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 4, pp. 1922–1939, Apr. 2018.

[17] X. Liu et al., “Deep multiple instance learning-based spatial–spectral classification for PAN and MS imagery,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 1, pp. 461–473, Jan. 2018.

[18] M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, “A new deep convolutional neural network for fast hyperspectral image classification,” ISPRS J. Photogram. Remote Sens., vol. 145, pp. 120–147, Nov. 2018.

[19] J. M. Haut, R. Fernandez-Beltran, M. E. Paoletti, J. Plaza, A. Plaza, and F. Pla, “A new deep generative network for unsupervised remote sensing single-image super-resolution,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 11, pp. 6792–6810, Nov. 2018.

[20] G. Cheng, J. Han, and X. Lu, “Remote sensing image scene classifi- cation: Benchmark and state of the art,” Proc. IEEE, vol. 105, no. 10, pp. 1865–1883, Oct. 2017.

[21] Z. Zheng, L. Zheng, and Y. Yang, “A discriminatively learned CNN embedding for person reidentification,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 4, no. 1, p. 13, 2017.

[22] J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah, “Signature verification using a ‘Siamese’ time delay neural network,” in Proc. Int. Conf. Neural Inf. Process. Syst., 1993, pp. 737–744.

[23] S. Chopra, R. Hadsell, and Y. Lecun, “Learning a similarity metric discriminatively, with application to face verification,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), vol. 1, Jun. 2005, pp. 539–546.

[24] L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. S. Torr, “Fully-convolutional Siamese networks for object tracking,” in Proc. Eur. Conf. Comput. Vis., 2016, pp. 850–865.

[25] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2009, pp. 248–255.

[26] G. Cheng, C. Yang, X. Yao, L. Guo, and J. Han, “When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 5, pp. 2811–2821, May 2018.

[27] Y. Yang and S. Newsam, “Bag-of-visual-words and spatial extensions for land-use classification,” in Proc. SIGSPATIAL Int. Conf. Adv. Geo- graphic Inf. Syst., 2010, pp. 270–279.

[28] B. Zhao, Y. Zhong, G.-S. Xia, and L. Zhang, “Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 4, pp. 2108–2123, Apr. 2016.

[29] J. Zhao et al., “3D fast convex-hull-based evolutionary multiobjective optimization algorithm,” Appl. Soft Comput., vol. 67, pp. 322–336, Jun. 2018.

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