[1] Tanha J, van Someren M, Afsarmanesh H. Semi-supervised self-training for decision tree classifiers[J]. International Journal of Machine Learning and Cybernetics, 8, 355-370(2017). http://link.springer.com/10.1007/s13042-015-0328-7
Tanha J, van Someren M, Afsarmanesh H. Semi-supervised self-training for decision tree classifiers[J]. International Journal of Machine Learning and Cybernetics, 8, 355-370(2017). http://link.springer.com/10.1007/s13042-015-0328-7
[2] Guo Y, Jia X, Remote Sensing, Spatial Information Sciences. IV-, 4/W2, 161-165(2017).
Guo Y, Jia X, Remote Sensing, Spatial Information Sciences. IV-, 4/W2, 161-165(2017).
[3] Xia M, Cao G, Wang G Y et al. Remote sensing image classification based on deep learning and conditional random fields[J]. Journal of Image and Graphics, 22, 1289-1301(2017).
Xia M, Cao G, Wang G Y et al. Remote sensing image classification based on deep learning and conditional random fields[J]. Journal of Image and Graphics, 22, 1289-1301(2017).
[4] Cao X Y, Xu L, Meng D Y et al. Integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification[J]. Neurocomputing, 226, 90-100(2017). http://dl.acm.org/citation.cfm?id=3031115.3031315
Cao X Y, Xu L, Meng D Y et al. Integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification[J]. Neurocomputing, 226, 90-100(2017). http://dl.acm.org/citation.cfm?id=3031115.3031315
[5] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017). http://dl.acm.org/citation.cfm?id=2999257
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017). http://dl.acm.org/citation.cfm?id=2999257
[6] Liu Y T, Li Z Q, Yang X L. Application of improved convolution neural network in remote sensing image classification[J]. Journal of Computer Applications, 38, 949-954(2018).
Liu Y T, Li Z Q, Yang X L. Application of improved convolution neural network in remote sensing image classification[J]. Journal of Computer Applications, 38, 949-954(2018).
[7] Yang H, Li L Q, Yang H H et al. Method of urban management cases' image classification based on convolutional neural network[J]. Computer Engineering and Applications, 54, 242-248, 266(2018).
Yang H, Li L Q, Yang H H et al. Method of urban management cases' image classification based on convolutional neural network[J]. Computer Engineering and Applications, 54, 242-248, 266(2018).
[8] Fu G Y, Gu H Y, Wang H Q. Spectral and spatial classification of hyperspectral images based on convolutional neural networks[J]. Science Technology and Engineering, 17, 268-274(2017).
Fu G Y, Gu H Y, Wang H Q. Spectral and spatial classification of hyperspectral images based on convolutional neural networks[J]. Science Technology and Engineering, 17, 268-274(2017).
[9] LeCun Y, Bottou L, Bengio Y et al. . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998). http://brain.oxfordjournals.org/lookup/external-ref?access_num=10.1109/5.726791&link_type=DOI
LeCun Y, Bottou L, Bengio Y et al. . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998). http://brain.oxfordjournals.org/lookup/external-ref?access_num=10.1109/5.726791&link_type=DOI
[10] Simonyan K. -04-10)[2018-10-20]. https:∥arxiv., org/abs/1409, 1556(2015).
Simonyan K. -04-10)[2018-10-20]. https:∥arxiv., org/abs/1409, 1556(2015).
[11] Eitel A, Springenberg J T, Spinello L et al. Multimodal deep learning for robust RGB-D object recognition. [C]∥2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 28-October 2, 2015, Hamburg, Germany. New York: IEEE, 15666832(2015).
Eitel A, Springenberg J T, Spinello L et al. Multimodal deep learning for robust RGB-D object recognition. [C]∥2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 28-October 2, 2015, Hamburg, Germany. New York: IEEE, 15666832(2015).
[12] Xu Y, Du J, Dai L R et al. Cross-language transfer learning for deep neural network based speech enhancement. [C]∥The 9th International Symposium on Chinese Spoken Language Processing, September 12-14, 2014, Singapore, Singapore. New York: IEEE, 14700579(2014).
Xu Y, Du J, Dai L R et al. Cross-language transfer learning for deep neural network based speech enhancement. [C]∥The 9th International Symposium on Chinese Spoken Language Processing, September 12-14, 2014, Singapore, Singapore. New York: IEEE, 14700579(2014).
[13] Han D M, Liu Q G, Fan W G. A new image classification method using CNN transfer learning and web data augmentation[J]. Expert Systems With Applications, 95, 43-56(2018). http://www.sciencedirect.com/science/article/pii/S0957417417307844
Han D M, Liu Q G, Fan W G. A new image classification method using CNN transfer learning and web data augmentation[J]. Expert Systems With Applications, 95, 43-56(2018). http://www.sciencedirect.com/science/article/pii/S0957417417307844
[14] Ahmed A, Yu K, Xu W et al. Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks[M]. ∥Forsyth D, Torr P, Zisserman A. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 69-82(2008).
Ahmed A, Yu K, Xu W et al. Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks[M]. ∥Forsyth D, Torr P, Zisserman A. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 69-82(2008).
[15] Camps-Valls G, Gomez-Chova L, Munoz-Mari J et al. Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection[J]. IEEE Transactions on Geoscience and Remote Sensing、, 46, 1822-1835(2008). http://ieeexplore.ieee.org/document/4509590
Camps-Valls G, Gomez-Chova L, Munoz-Mari J et al. Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection[J]. IEEE Transactions on Geoscience and Remote Sensing、, 46, 1822-1835(2008). http://ieeexplore.ieee.org/document/4509590
[16] Ding X, Jiang Y, Huang Y et al. Pan-sharpening with a Bayesian nonparametric dictionary learning model. [C]∥Artificial Intelligence and Statistics. [S.l.]: [s.n.], 176-184(2014).
Ding X, Jiang Y, Huang Y et al. Pan-sharpening with a Bayesian nonparametric dictionary learning model. [C]∥Artificial Intelligence and Statistics. [S.l.]: [s.n.], 176-184(2014).
[17] Hane C, Zach C, Cohen A et al. Joint 3D scene reconstruction and class segmentation. [C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 13824292(2013).
Hane C, Zach C, Cohen A et al. Joint 3D scene reconstruction and class segmentation. [C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 13824292(2013).
[18] Fang X, Wang G H, Yang H C et al. High resolution remote sensing image classification combining with mean-shift segmentation and fully convolution neural network[J]. Laser & Optoelectronics Progress, 55, 022802(2018).
Fang X, Wang G H, Yang H C et al. High resolution remote sensing image classification combining with mean-shift segmentation and fully convolution neural network[J]. Laser & Optoelectronics Progress, 55, 022802(2018).
[19] Tu S Q, Xue Y J, Liang Y et al. Review on RGB-D image classification[J]. Laser & Optoelectronics Progress, 53, 060003(2016).
Tu S Q, Xue Y J, Liang Y et al. Review on RGB-D image classification[J]. Laser & Optoelectronics Progress, 53, 060003(2016).
[20] He M Y, Cheng Y L, Liao X J et al. Building extraction algorithm by fusing spectral and geometrical features[J]. Laser & Optoelectronics Progress, 55, 042803(2018).
He M Y, Cheng Y L, Liao X J et al. Building extraction algorithm by fusing spectral and geometrical features[J]. Laser & Optoelectronics Progress, 55, 042803(2018).
[21] Couprie C, Farabet C, Najman L et al. -03-14)[2018-10-20]. https:∥arxiv., org/abs/1301, 3572(2013).
Couprie C, Farabet C, Najman L et al. -03-14)[2018-10-20]. https:∥arxiv., org/abs/1301, 3572(2013).
[22] Wang W, Yang X Y, Ooi B C et al. Effective deep learning-based multi-modal retrieval[J]. The VLDB Journal, 25, 79-101(2016). http://dl.acm.org/citation.cfm?id=2884421
Wang W, Yang X Y, Ooi B C et al. Effective deep learning-based multi-modal retrieval[J]. The VLDB Journal, 25, 79-101(2016). http://dl.acm.org/citation.cfm?id=2884421
[23] Cheng G, Han J W. A survey on object detection in optical remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28(2016). http://arxiv.org/abs/1603.06201v1
Cheng G, Han J W. A survey on object detection in optical remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28(2016). http://arxiv.org/abs/1603.06201v1
[24] Cheng G, Zhou P C, Han J W. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 7405-7415(2016). http://ieeexplore.ieee.org/document/7560644/
Cheng G, Zhou P C, Han J W. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 7405-7415(2016). http://ieeexplore.ieee.org/document/7560644/
[25] Zhou R T, Li Z P, Wu C et al. Buddy routing: a routing paradigm for NanoNets based on physical layer network coding. [C]∥2012 21st International Conference on Computer Communications and Networks (ICCCN), July 30-August 2, 2012, Munich, Germany. New York: IEEE, 12965366(2012).
Zhou R T, Li Z P, Wu C et al. Buddy routing: a routing paradigm for NanoNets based on physical layer network coding. [C]∥2012 21st International Conference on Computer Communications and Networks (ICCCN), July 30-August 2, 2012, Munich, Germany. New York: IEEE, 12965366(2012).