References
- Bachmann, C. M., Ainsworth, T. L., & Fusina, R. A. (2005). Exploiting manifold geometry in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 441–454.
- Benediktsson, J. A., & Swain, P. H. (1992). Consensus theoretic classification methods. IEEE Transactions on Systems, Man, and Cybernetics, 22(4), 688–704.
- Bioucas-Dias, J. M, & Nascimento, J. M. (2008). Hyperspectral subspace identification. Ieee Transactions on Geoscience and Remote Sensing, 46(8), 2435–2445. doi:10.1109/TGRS.2008.918089
- Cahil, N. D., Chew, S. E., & Wenger., P. S. (2015). Spatial-spectral dimensionality reduction of hyperspectral imagery with partial knowledge of class labels. Proceeding SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, 9742, 4905–4922.
- Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014a). Return of the devil in the details: delving deep into convolutional nets. In British Machine Vision Conference.
- Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014b). Return of the devil in the details: Delving deep into convolutional nets. CoRR abs/1405.3531. Retrieved from http://arxiv.org/abs/1405.3531
- Chen, C., Li, W., Su, H., & Liu, K. (2014). Spectral-spatial classification of hyperspectral image based on Kernel extreme learning machine. Remote Sensing, 6(6), 5795–5814. Retrieved from http://www.mdpi.com/2072-4292/6/6/5795
- Cheng, G., Li, Z., Han, J., Yao, X., & Guo., L. (2018). Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 56(11), 0196–2892.
- Cheng, G., Yang, C., Yao, X., Guo, L., & Han, J. (2018). When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2811–2821.
- Crawford, M. M., Ma, L., & Kim., W. (2011). Exploring nonlinear manifold learning for classification of hyperspectral data (Vol. 3, pp. 207–234).
- Das, D., & George Lee, C. S. (2018a, October). Unsupervised domain adaptation using regularized hyper-graph matching.” In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 3758–3762).
- Das, D., & George Lee, C. S. (2018b, October). Graph matching and pseudo-label guided deep unsupervised domain adaptation.” In 2018 27th International Conference on Artificial Neural Networks Rhodes (pp. 342–352). Greece.
- Das, D., & George Lee, C. S. (2018c). Sample-to-sample correspondence for unsupervised domain adaptation. Engineering Applications of Artificial Intelligence, 73, 80–91. Retrieved from http://www.sciencedirect.com/science/article/pii/S0952197618301088
- Hao, S., Wang, W., Ye, Y., Nie, T., & Bruzzone, L. (2018). Two-stream deep architecture for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 2349–2361.
- He, J., Zhang, L., Wang, Q., & Li, Z. (2009). Using diffusion geometric coordinates for hyperspectral imagery representation. IEEE Geoscience and Remote Sensing Letters, 6(4), 767–771.
- He, L., Li, J., Liu, C., & Li, S. (2018). Recent advances on spectralspatial hyperspectral image classification: An overview and new guidelines. IEEE Transactions on Geoscience and Remote Sensing, 56(3), 1579–1597.
- Hu, W., Huang, Y., Wei, L., Zhang, F., & Li, H. (2015). Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015(258619), 12.
- Jiao, L., Liang, M., Chen, H., Yang, S., Liu, H., & Cao, X. (2017). Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5585–5599.
- Khodadadzadeh, M., Li, J., Plaza, A., Ghassemian, H., & Bioucas-Dias., J. M. (2014). SpectralSpatial classification of hyperspectral data using local and global probabilities for mixed pixel characterization. IEEE Transactions on Geoscience and Remote Sensing, 52(10), 6298–6314.
- Krishnapuram, B., Carin, L., Figueiredo, M. A. T., & Hartemink, A. J. (2005). Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 957–968.
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Neural Information Processing Systems, Lake Tahoe, Nevada, 1097–1105.
- Landgrebe, D. A. (2005). Signal theory methods in multispectral remote sensing. Newark, NJ: Wiley. Retrieved from https://cds.cern.ch/record/995182
- Li, S., Ni, L., Jia, X., Gao, L., Zhang, B., & Man, P. (2016). Multi-scale superpixel spectralspatial classification of hyperspectral images. International Journal of Remote Sensing, 37(20), 4905–4922.
- Li, W., Chen, C., Su, H., & Du, Q. (2015). Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3681–3693.
- Li, W., Prasad, S., Fowler, J. E., & Bruce, L. M. (2012). Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 50(4), 1185–1198.
- Li, W., Wu, G., Zhang, F., & Du, Q. (2017a). Hyperspectral image classification using deep pixel-pair features. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 844–853.
- Li, W., Wu, G., Zhang, F., & Du, Q. (2017b). Hyperspectral image classification using deep pixel-pair features. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 844–853.
- Liu, B., Yu, X., Zhang, P., Yu, A., Fu, Q., & Wei, X. (2018). Supervised deep feature extraction for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 1909–1921.
- Liu, T., Gu, Y., Chanussot, J., & Dalla Mura, M. (2017). Multimorphological Superpixel model for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 6950–6963.
- Lu, X., Chen, Y., & Li, X. (2018). Hierarchical recurrent neural hashing for image retrieval with hierarchical convolutional features. IEEE Transactions on Image Processing, 27(1), 106–120.
- Lunga, D., Prasad, S., Crawford, M. M., & Ersoy, O. (2014). Manifold-learning-based feature extraction for classification of hyperspectral data: A review of advances in manifold learning. IEEE Signal Processing Magazine, 31(1), 55–66.
- Ma, L., Crawford, M. M., & Tian, J. (2010). Anomaly detection for hyperspectral images based on Robust locally linear embedding. Journal of Infrared, Millimeter and Terahertz Waves, 31, 753–762.
- Saranathan, A. M., & Parente, M. (2016). Uniformity-based superpixel segmentation of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 54(3), 1419–1430.
- Silva, V., & Tenenbaum, J. B. (2003). Global versus local methods in nonlinear dimensionality reduction. Advances in Neural Information Processing Systems, 15, 721–728.
- Sun, L., Wu, Z., Liu, J., Xiao, L., & Wei, Z. (2015). Supervised spectralspatial hyperspectral image classification with weighted Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, 53(3), 1490–1503.
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … Rabinovich., A. (2015, June). “Going deeper with convolutions.” In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts (pp. 1–9).
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. E., Anguelov, D., … Andrew, R. (2014). Going deeper with convolutions. CoRR abs/1409.4842. Retrieved from http://arxiv.org/abs/1409.4842
- Xie, J., Dai, G., Zhu, F., Wong, E. K., & Fang, Y. (2017). DeepShape: Deep-learned shape descriptor for 3D shape retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(7), 1335–1345.
- Yao, C., Han, J., Nie, F., Xiao, F., & Li, X. (2018). Local regression and global information-embedded dimension reduction. IEEE Transactions on Neural Networks and Learning Systems, 29(10), 4882–4893.
- Zhao, C., Wan, X., Zhao, G., Cui, B., Liu, W., & Qi., B. (2017). Spectral-spatial classification of hyperspectral imagery based on stacked sparse autoencoder and random forest. European Journal of Remote Sensing, 50(1), 47–63.