Citation: Haifeng Song, Weiwei Yang, Songsong Dai, Lei Du, Yongchen Sun. Using dual-channel CNN to classify hyperspectral image based on spatial-spectral information[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3450-3477. doi: 10.3934/mbe.2020195
[1] | T. V. V Bandos, L. Bruzzone, G. Camps-Valls, Classification of hyperspectral images with regularized linear discriminant analysis, IEEE Trans. Geosci. Remote Sens., 47 (2009), 862-873. |
[2] | G. Licciardi, P. R. Marpu, J. Chanussot, J. A. Benediktsson, Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles, IEEE Geosci. Remote Sens. Lett., 9 (2012), 447-451. |
[3] | A. Villa, J. A. Benediktsson, J. Chanussot, C. Jutten, Hyperspectral image classification with Independent component discriminant analysis, IEEE Transact. Geosci. Remote Sens., 49 (2011), 4865-4876. |
[4] | H. Bischof, A. Leonardis, Finding optimal neural networks for land use classification, NIeee Trans. Geosci. Remote Sens., 36 (1998), 337-341. |
[5] | G. Camps-Valls, L. Gomez-Chova, J. Calpe-Maravilla, J. D. Martin-Guerrero, E. Soria-Olivas, L. Alonso-Chorda, et al., Robust support vector method for hyperspectral data classification and knowledge discovery, IEEE Trans. Geosci. Remote Sens., 42 (2004), 1-13. |
[6] | D. L. Civco, Artificial neural networks for land-cover classification and mapping, Int. J. Geogr. Inf. Syst., 7 (1993), 173-186. |
[7] | F. Melgani, L. Bruzzone, Classification of hyperspectral remote sensing images with support vector machines, IEEE Trans. Geosci. Remote Sens., 42 (2004), 1778-1790. |
[8] | S. Haifeng, C. Guangsheng, W. Hairong, Y. Weiwei, The improved (2D)2PCA algorithm and its parallel implementation based on image block, Microprocess. Microsyst., 47 (2016), 170-177. |
[9] | Y. Chen, Z. Lin, X. Zhao, G. Wang, Y. Gu, Deep learning-based classification of hyperspectral data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7 (2014), 2094-2107. |
[10] | X. Chen, S. Xiang, C. L. Liu, C. H. Pan, Vehicle detection in satellite images by hybrid deep convolutional neural networks, IEEE Geosci. Remote Sens. Lett., 11 (2014), 1797-1801. |
[11] | Z. Feng, S. Yang, S. Wang, L. Jiao Discriminative spectral-spatial margin-based semisupervised dimensionality reduction of hyperspectral data, IEEE Trans. Geosci. Remote Sens., 12 (2014), 224-228. |
[12] | Z. Wang, N. M. Nasrabadi, T. S. Huang, Spatial-spectral classification of hyperspectral images using discriminative dictionary designed by learning vector quantization, IEEE Trans. Geosci. Remote Sens., 52 (2014), 4808-4822. |
[13] | S. Bernabe, P. R. Marpu, A. Plaza, M. D. Mura, J. A. Benediktsson, Spectral-spatial classification of multispectral images using kernel feature space representation, IEEE Geosci. Remote Sens. Lett., 11 (2013), 228-292. |
[14] | R. Ji, Y. Gao, R. Hong, Q. Liu, D. Tao, X. Li, Spectral-spatial constraint hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 3 (2014), 1811-1824. |
[15] | M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, J. C. Tilton, Advances in spectralspatial classification of hyperspectral images, Proc. IEEE, 101 (2013), 652-675. |
[16] | W. Zhao, S. Du, Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach, IEEE Trans. Geosci. Remote Sens., 54 (2016), 4544-4554. |
[17] | J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. M. Nasrabadi, J. Chanussot, Hyperspectral remote sensing data analysis and future challenges, IEEE Geosci. Remote Sens. Mag., 1 (2013), 6-36. |
[18] | G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Calpe-Maravilla, Composite kernels for hyperspectral image classification, IEEE Geosci. Remote Sens. Lett., 3 (2006), 93-97. |
[19] | J. Li, P. R. Marpu, A. Plaza, J. M. Bioucas-Dias, J. A. Benediktsson, Generalized composite kernel framework for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 51 (2013), 4816-4829. |
[20] | Jun Li, José M. Bioucas-Dias, Antonio Plaza, Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning, IEEE Transact. Geosci. Remote Sens., 51 (2013), 844-856. |
[21] | Z. Zhong, B. Fan, J. Duan, et al. Discriminant tensor spectral-spatial feature extraction for hyperspectral image classification, IEEE Geosci. Remote Sens. Lett., 12 (2015), 1028-1032. |
[22] | X. Kang, S. Li, J. A. Benediktsson, Spectral-spatial hyperspectral image classification with edgepreserving filtering, IEEE Transact. Geosci. Remote Sens., 52 (2014), 2666-2677. |
[23] | Y. Zhou, J. Peng, C. L. P. Chen, Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 53 (2015), 1082-1095. |
[24] | H. Zhang, Y. Li, Y. Zhang, Q. Shen, Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network, Remote Sens. Lett., 8 (2017), 438-447. |
[25] | Y. Chen, X. Zhao, X. Jia, Spectral-spatial classification of hyperspectral data based on deep belief network, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8 (2015), 2381-2392. |
[26] | W. Hu, Y. Huang, L. Wei, F. Zhang, H. Li, Deep convolutional neural networks for hyperspectral image classification, J. Sensor., 2015 (2015), 1-12. |
[27] | Z. Lin, Y. Chen, X. Zhao, G. Wang, Spectral-spatial classification of hyperspectral image using autoencoders, 2013 9th International Conference on Information, Communications & Signal Processing, Volume (2014). |
[28] | P. Ghamisi, J. Plaza, Y. Chen, J. Li, A. J. Plaza, Advanced spectral classifiers for hyperspectral images: A review, IEEE Geosci. Remote Sens. Mag., 5 (2017), 8-32. |
[29] | L. Zhang, L. Zhang, B. Du, Deep learning for remote sensing data: A technical tutorial on the state of the art, IEEE Geosci. Remote Sens. Mag., 4 (2016), 22-40. |
[30] | Y. Lecun, Y. Bengio, G. Hinton, ImageNet classification with deep convolutional neural networks, Nature, 521 (2015), 436. |
[31] | W. Li, G. Wu, F. Zhang, Q. Du, Hyperspectral image classification using deep pixel-pair features, IEEE Transact. Geosci. Remote Sens., 55 (2017), 844-853. |
[32] | W. Zhao, S. Du, Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach, IEEE Transact. Geosci. Remote Sens., 54 (2016), 4544-4554. |
[33] | Y. Chen, H. Jiang, C. Li, P. Ghamisi, Deep feature extraction and classification of hyperspectral images based on convolutional neural networks, IEEE Transact. Geosci. Remote Sens., 54 (2016), 6232-6251. |
[34] | T. V. Nguyen, L. Liu, K. Nguyen, Exploiting generic multi-level convolutional neural networks for scene understanding, 2016 14th International Conference on Control, Automation, Robotics & Vision, 23 (2016), 1-6. |
[35] | X. Cao, F. Zhou, L. Xu, D. Meng, Z. Xu, J. Paisley, Hyperspectral image classification with markov random fields and a convolutional neural network, IEEE Trans. Image Process., 27 (2018), 2354-2367. |
[36] | Y. Chen, Z. Lin, X. Zhao, G. Wang, Y. Gu, Deep learning-based classification of hyperspectral data, IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 7 (2014), 2094-2107. |
[37] | Jing Weipeng, Huo Shuaiqi, Miao Qiucheng, Chen Xuebin, A Model of Parallel Mosaicking for Massive Remote Sensing Images Based on Spark, IEEE Access, 99 (2017), 18229-18237. |