[1] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 18, 1527-1554(2006). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6796673

[2] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C]. IEEE Conference on Computer Vision and Pattern Recognition, 1-9(2015).

[3] 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).

[4] Fang X, Wang G H, Yang H C et al. High resolution remote sensing image classification combing with mean-shift segmentation and full convolution neural network[J]. Laser & Optoelectronics Progress, 55, 022802(2018).

[5] He X F, Zou Z R, Tao C et al. Combined saliency with multi-convolutional neural network for high resolution remote sensing scene classification[J]. Acta Geodaetica et Cartographica Sinica, 45, 1073-1080(2016).

[6] Le Q V, Ranzato M, Monga R et al. Building high-level features using large scale unsupervised learning[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, 8595-8598(2013).

[7] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 313, 504-507(2006). http://www.ncbi.nlm.nih.gov/pubmed/16873662

[8] Agarwal A, El-Ghazawi T, El-Askary H et al. Efficient hierarchical-PCA dimension reduction for hyperspectral imagery[C]. IEEE International Symposium on Signal Processing and Information Technology, 353-356(2008).

[9] Pan Z J, Rust A G, Bolouri H. Image redundancy reduction for neural network classification using discrete cosine transforms[C]. IEEE-INNS-ENNS International Joint Conference on Neural Networks, 3, 3149-3155(2000).

[10] Dabbaghchian S, Ghaemmaghami M P, Aghagolzadeh A. Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology[J]. Pattern Recognition, 43, 1431-1440(2010). http://dl.acm.org/citation.cfm?id=1710206

[11] Sun J P, Liu J Q. Coal and rock recognition method based on low frequency component characteristics of discrete cosine transform and learning vector quantization[J]. Industry & Mine Automation, 41, 1-6(2015).

[12] Zou X Y, Xu X M, Qing C M et al. High speed deep networks based on Discrete Cosine Transformation[C]. IEEE International Conference on Image Processing, 5921-5925(2014).

[13] Yan Z Z, Liu J J[J]. Improved image compression coding method based on discrete cosine transform Computer Technology and Development, 2016, 147-149.

[14] Dai C N. SVM visual classification based on weighted feature of genetic algorithm[C]. IEEE Sixth International Conference on Intelligent Systems Design and Engineering Applications, 786-789(2015).

[15] 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(2014). http://ieeexplore.ieee.org/document/6895132/

[16] Xu S H, Mu X D, Zhao P et al. Scene classification of remote sensing image based on multi-scale feature and deep neural network[J]. Acta Geodaetica et Cartographica Sinica, 45, 834-840(2016).