[1] Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 36, 0428001(2016).
[2] Chen S Z, Tian Y L. Pyramid of spatial relatons for scene-level land use classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 1947-1957(2015). http://ieeexplore.ieee.org/document/6895132/
[3] Zhang L P, Zhang L F, Du B. Deep learning for remote sensing data: a technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 4, 22-40(2016). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7486259
[4] Li A X, Lu Z W, Wang L W et al. Zero-shot scene classification for high spatial resolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 4157-4167(2017). http://ieeexplore.ieee.org/document/7902107/
[5] Xian Y Q, Akata Z, Sharma G et al. Latent embeddings for zero-shot classification. [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 69-77(2016).
[6] Wang D H, Li Y, Lin Y T et al. Relational knowledge transfer for zero-shot learning. [C]//Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), February 12-17, 2016, Phoenix, Arizona, USA. California: AAAI Publications, 2145-2151(2016).
[7] Zhang Z M, Saligrama V. Zero-shot learning via joint latent similarity embedding. [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 6034-6042(2016).
[8] Zhang Z M, Saligrama V. Zero-shot learning via semantic similarity embedding. [C]//2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 4166-4174(2015).
[9] Wang Q, Chen K. Zero-shot visual recognition via bidirectional latent embedding[J]. International Journal of Computer Vision, 124, 356-383(2017). http://link.springer.com/10.1007/s11263-017-1027-5
[10] Li Y N, Wang D H, Hu H H et al. Zero-shot recognition using dual visual-semantic mapping paths. [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 5207-5215(2017).
[11] Kodirov E, Xiang T, Fu Z Y et al. Unsupervised domain adaptation for zero-shot learning. [C]//2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2452-2460(2015).
[12] Guo Y C, Ding G G, Jing X M et al. Transductive zero-shot recognition via shared model space learning. [C]//Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA. California: AAAI Publications, 3494-3500(2016).
[13] Fu Y W, Hospedales T M, Xiang T et al. Transductive multi-view embedding for zero-shot recognition and annotation[M]. //Fleet D, Pajdla T, Schiele B, et al. Lecture notes in computer science 2014. Cham: Springer, 8690, 584-599(2014).
[14] Ji Z, Xie Y Z, Pang Y W. Zero-shot learning based on canonical correlation analysis and distance metric learning[J]. Journal of Tianjin University, 50, 813-820(2017).
[15] Ji Z, Sun T, Yu Y L. Transductive discriminative dictionary learning approach for zero-shot classification[J]. Journal of Software, 28, 2961-2970(2017).
[16] Bao C L, Ji H, Quan Y H et al. Dictionary learning for sparse coding: algorithms and convergence analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 1356-1369(2016). http://ieeexplore.ieee.org/document/7293682
[17] Yang M, Chang H Y, Luo W X. Discriminative analysis-synthesis dictionary learning for image classification[J]. Neurocomputing, 219, 404-411(2017). http://www.sciencedirect.com/science/article/pii/S0925231216310657
[18] Wang J J, Guo Y Q, Guo J et al. Synthesis linear classifier based analysis dictionary learning for pattern classification[J]. Neurocomputing, 238, 103-113(2017). http://www.sciencedirect.com/science/article/pii/S0925231217301157
[19] Ravishankar S, Bresler Y. Sparsifying transform learning with efficient optimal updates and convergence guarantees[J]. IEEE Transactions on Signal Processing, 63, 2389-2404(2015). http://ieeexplore.ieee.org/document/7045534/
[20] Kreutz-Delgado K, Murray J F, Rao B D et al. Dictionary learning algorithms for sparse representation[J]. Neural Computation, 15, 349-396(2003). http://www.ncbi.nlm.nih.gov/pubmed/12590811
[21] Fernando B, Fromont E, Muselet D et al. Discriminative feature fusion for image classification. [C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 3434-3441(2012).
[22] Tenenbaum J B. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 290, 2319-2323(2000). http://www.jstor.org/stable/3081721
[23] Yang Y, Newsam S. Bag-of-visual-words and spatial extensions for land-use classification. [C]//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2-5, 2010, San Jose, California. New York: ACM, 270-279(2010).
[24] Zou Q, Ni L H, Zhang T et al. Deep learning based feature selection for remote sensing scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 12, 2321-2325(2015). http://ieeexplore.ieee.org/document/7272047/
[25] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions. [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, 2015, Boston, MA, USA. New York: IEEE, 7298594(2015).
[26] Simonyan K. -04-10)[ 2019-01-01][EB/OL]. Zisserman A. Very deep convolutional networks for large-scale image recognition., org/abs/1409, 1556(2015). https://arxiv.