[1] [1] Luo B, Zhang L. Robust autodual morphological profiles for the classification of high-resolution satellite images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1451-1462.

[2] [2] Pacifici F, Chini M, Emery W J. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification[J]. Remote Sensing of Environment, 2009, 113(6): 1276-1292.

[3] [3] Huang X, Zhang L. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of highresolution remotely sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 257-272.

[4] [4] Liu C, Hong L, Chen J, et al.. Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image[J]. Journal of Remote Sensing, 2015, 19(2): 228-239.

[5] [5] Bengio Y. Learning deep architectures for AI[J]. Foundations and Trends in Machine Learning, 2009, 2(1): 1-127.

[6] [6] Bottou L. Large-scale kjernel machines[M]. Cambridge: MIT Press, 2007: 321-359.

[7] [7] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.

[8] [8] Yu D, Deng L. Deep learning and its applications to signal and information processing[J]. IEEE Signal Processing Magazine, 2011, 28 (1): 145-154.

[9] [9] Jones N. The learning machines[J]. Nature, 2014, 505(7842): 146-148.

[10] [10] Yu Kai, Jia Lei, Chen Yuqiang, et al.. Deep learning: Yesterday, today and tomorrow[J]. Journal of Computer Research and Development, 2013, 50(9): 1799-1804.

[11] [11] Hinton G. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8): 1771-1800.

[12] [12] Jin Jing, Zou Zhengrong, Tao Chao. Compressed texton based high resolution remote sensing image classification[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(5): 493-499.

[13] [13] Do M N, Vetterli M. The Contourlet transform: An efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.

[14] [14] Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform: Theory, design, and applications[J]. Image Processing, IEEE Transactions on, 2006, 15(10): 3089-3101.

[15] [15] Zhou J, Cunha A L, Do M N. Nonsubsampled contourlet transform: Construction and application in enhancement[C]. Image Processing, 2005. ICIP 2005. IEEE International Conference on, IEEE, 2005, 1: 469-472.

[16] [16] Wu Xiaoyue, Guo Baolong, Tang Lu, et al.. A new adaptive image denoising method based on the nonsubsampled Contourlet transform algorithm[J]. Acta Optica Sinica, 2009, 29(8): 2147-2152.

[17] [17] Zhou Jinghong, Zhou Cui, Zhu Jianjun, et al.. A method of super-resolution reconstruction for remote sensing image based on nonsubsampled Contourlet transform[J]. Acta Optica Sinica, 2015, 35(1): 0110001.

[18] [18] He Yongcong, Liu Wenbo, Zhang Gong, et al.. Rotation-invariant texture image retrieval algorithm based on non-subsampled Contourlet transform[J]. Journal of Image and Graphics, 2011, 16(1): 79-83.

[19] [19] Wu Yiquan, Yin Jun, Zhu Li, et al.. Method for improving reconstructed image quality of digital hologram based on SRAD and NSCT[J]. Chinese J Lasers, 2014, 41(2): 0209024.

[20] [20] Zhou Liangji, Li Qingwu, Huo Guanying, et al.. Fase image recognition method based on the NSCT and bionic pattern[J]. Laster & Optoelectronics Progress, 2015, 52(3): 031001.

[21] [21] Chen Musheng, Cai Zhishan. Study on fusion of visual and infrared images based on NSCT[J]. Laster & Optoelectronics Progress, 2015, 52(6): 061002.

[22] [22] Huang Xin. Multiscale texture and shape feature extraction and object-oriented classification for very high resolution remotely sensed imagery[D]. Wuhan: Wuhan University, 2009: 71-76.

[23] [23] Hinton G. A practical guide to training restricted Boltzmann machines[J]. Momentum, 2010, 9(1): 926.

[24] [24] Roux N L, Bengio Y. Representational power of restricted Boltzmann machines and deep belief networks[J]. Neural Computation, 2008, 20(6): 1631-1649.

[25] [25] Larochelle H, Bengio Y, Louradour J, et al.. Exploring strategies for training deep neural networks[J]. Journal of Machine Learning Research, 2009, 10(1): 1-40.