A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification

References

• Abdel-Hamid, O., A. R. Mohamed, H. Jiang, and G. Penn. 2012. “Applying Convolutional Neural Networks Concepts to Hybrid Nn-Hmm Model for Speech Recognition.” In 2012 Ieee International Conference on Acoustics, Speech and Signal Processing (Icassp), Kyoto, March 25–30. 4277–4280, doi:10.1109/ICASSP.2012.6288864.
   Google Scholar
• Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” Isprs Journal of Photogrammetry and Remote Sensing 65 (1): 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
   Web of Science ®Google Scholar
• Bouvrie, J. 2014. “Notes on convolutional neural networks.” http://people.csail.mit.edu/jvb/papers/cnn_tutorial.pdf:1-9
   Google Scholar
• Bruna, J., and S. Mallat. 2013. “Invariant Scattering Convolution Networks.” Ieee Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1872–1886. doi:10.1109/Tpami.2012.230.
   PubMed Web of Science ®Google Scholar
• Chen, S. Z., H. P. Wang, F. Xu, and Y. Q. Jin. 2016. “Target Classification Using the Deep Convolutional Networks for SAR Images.” Ieee Transactions on Geoscience and Remote Sensing 54 (8): 4806–4817. doi:10.1109/Tgrs.2016.2551720.
   Web of Science ®Google Scholar
• Chen, X. Y., S. M. Xiang, C. L. Liu, and C. H. Pan. 2014a. “Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks.” Ieee Geoscience and Remote Sensing Letters 11 (10): 1797–1801. doi:10.1109/Lgrs.2014.2309695.
   Web of Science ®Google Scholar
• Chen, Y. S., Z. H. Lin, X. Zhao, G. Wang, and Y. F. Gu. 2014b. “Deep Learning-Based Classification of Hyperspectral Data.” Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2094–2107. doi:10.1109/Jstars.2014.2329330.
   Web of Science ®Google Scholar
• Geng, J., J. C. Fan, H. Y. Wang, X. R. Ma, B. M. Li, and F. L. Chen. 2015. “High-Resolution SAR Image Classification via Deep Convolutional Autoencoders.” Ieee Geoscience and Remote Sensing Letters 12 (11): 2351–2355. doi:10.1109/Lgrs.2015.2478256.
   Web of Science ®Google Scholar
• Hinton, G. E., S. Osindero, and Y. W. Teh. 2006. “A Fast Learning Algorithm for Deep Belief Nets.” Neural Computation 18 (7): 1527–1554. doi:10.1162/neco.2006.18.7.1527.
   PubMed Web of Science ®Google Scholar
• Hu, W., Y. Y. Huang, L. Wei, F. Zhang, and H. C. Li. 2015. “Deep Convolutional Neural Networks for Hyperspectral Image Classification.” Journal of Sensors 2015: 1–12. doi:10.1155/2015/258619.
   Web of Science ®Google Scholar
• Huang, W., L. Xiao, Z. H. Wei, H. Y. Liu, and S. Z. Tang. 2015. “A New Pan-Sharpening Method With Deep Neural Networks.” Ieee Geoscience and Remote Sensing Letters 12 (5): 1037–1041. doi:10.1109/Lgrs.2014.2376034.
   Web of Science ®Google Scholar
• Huble, D. H., and T. N. Wiesel. 1962. “Receptive Fields Binocular Interaction and Functional Architecture in the Cat Visual Cortex.” The Journal of Physiology 160 (1): 106–154. doi:10.1113/jphysiol.1962.sp006837.
   PubMed Web of Science ®Google Scholar
• Ijjina, E. P., and C. K. Mohan. 2016. “Classification of Human Actions Using Pose-Based Features and Stacked Auto Encoder.” Pattern Recognition Letters 83: 268–277. doi:10.1016/j.patrec.2016.03.021.
   Web of Science ®Google Scholar
• Johnson, B., and Z. X. Xie. 2013. “Classifying a High Resolution Image of an Urban Area Using Super-Object Information.” Isprs Journal of Photogrammetry and Remote Sensing 83: 40–49. doi:10.1016/j.isprsjprs.2013.05.008.
   Web of Science ®Google Scholar
• Kavukcuoglu, K., P. Sermanet, Y. L. Boureau, K. Gregor, M. Mathieu, and Y. LeCun. 2010. “Learning Convolutional Feature Hierachies for Visual Recognition”.” Advances in Neural Information Processing Systems 23: 82–90.
   Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, December 3–26, 1097–1105.
   Google Scholar
• Liu, D. S., and F. Xia. 2010. “Assessing Object-Based Classification: Advantages and Limitations.” Remote Sensing Letters 1 (4): 187–194. doi:10.1080/01431161003743173.
   Web of Science ®Google Scholar
• Liu, Y. Z., G. Cao, Q. S. Sun, and M. Siegel. 2015. “Hyperspectral Classification via Deep Networks and Superpixel Segmentation.” International Journal of Remote Sensing 36 (13): 3459–3482. doi:10.1080/01431161.2015.1055607.
   Web of Science ®Google Scholar
• Long, J., E. Shelhamer, and T. Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” In 2015 IEEE Conference on Computer Vision and Pattern Recognition (Cvpr), Boston, June 7–12, 3431–3440. doi:10.1109/CVPR.2015.7298965.
   Google Scholar
• Lv, Q., X. Niu, Y. Dou, J. Q. Xu, and Y. W. Lei. 2016. “Classification of Hyperspectral Remote Sensing Image Using Hierarchical Local-Receptive-Field-Based Extreme Learning Machine.” Ieee Geoscience and Remote Sensing Letters 13 (3): 434–438. doi:10.1109/Lgrs.2016.2517178.
   Web of Science ®Google Scholar
• Ma, X. R., H. Y. Wang, and J. Geng. 2016. “Spectral-Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder.” Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (9): 4073–4085. doi:10.1109/Jstars.2016.2517204.
   Web of Science ®Google Scholar
• Marmanis, D., M. Datcu, T. Esch, and U. Stilla. 2016. “Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks.” Ieee Geoscience and Remote Sensing Letters 13 (1): 105–109. doi:10.1109/Lgrs.2015.2499239.
   Web of Science ®Google Scholar
• Othmana, E., Y. Bazi, N. Alajlan, H. Alhichri, and F. Melgani. 2016. “Using Convolutional Features and a Sparse Autoencoder for Land-Use Scene Classification.” International Journal of Remote Sensing 37 (10): 2149–2167. doi:10.1080/01431161.2016.1171928.
   Web of Science ®Google Scholar
• Pan, X., S. Q. Zhang, H. Q. Zhang, X. D. Na, and X. F. Li. 2010. “A Variable Precision Rough Set Approach to the Remote Sensing Land Use/Cover Classification.” Computers & Geosciences 36 (12): 1466–1473. doi:10.1016/j.cageo.2009.11.010.
   Web of Science ®Google Scholar
• Romero, A., C. Gatta, and G. Camps-Valls. 2016. “Unsupervised Deep Feature Extraction for Remote Sensing Image Classification.” Ieee Transactions on Geoscience and Remote Sensing 54 (3): 1349–1362. doi:10.1109/Tgrs.2015.2478379.
   Web of Science ®Google Scholar
• Sainath, T. N., B. Kingsbury, G. Saon, H. Soltau, A.-R. Mohamed, G. Dahl, and B. Ramabhadran. 2015. “Deep Convolutional Neural Networks for Large-Scale Speech Tasks.” Neural Networks 64: 39–48. doi:10.1016/j.neunet.2014.08.005.
   PubMed Web of Science ®Google Scholar
• Silver, D., A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, et al. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529 (7587): 484-+. doi:10.1038/nature16961.
   PubMed Web of Science ®Google Scholar
• Tootell, R. B. H., E. Switkes, M. S. Silverman, and S. L. Hamilton. 1988. “Functional Anatomy of Macaque Striate Cortes.” The Journal of Neuroscience 8: 1531–1568.
   PubMed Web of Science ®Google Scholar
• Tuia, D., E. Pasolli, and W. J. Emery. 2011. “Using Active Learning to Adapt Remote Sensing Image Classifiers.” Remote Sensing of Environment 115 (9): 2232–2242. doi:10.1016/j.rse.2011.04.022.
   Web of Science ®Google Scholar
• Wang, J., J. W. Song, M. Q. Chen, and Z. Yang. 2015. “Road Network Extraction: A Neural-Dynamic Framework Based on Deep Learning and a Finite State Machine.” International Journal of Remote Sensing 36 (12): 3144–3169. doi:10.1080/01431161.2015.1054049.
   Web of Science ®Google Scholar
• Wang, Y. Q., Z. G. Xie, K. Xu, Y. Dou, and Y. W. Lei. 2016. “An Efficient and Effective Convolutional Auto-Encoder Extreme Learning Machine Network for 3d Feature Learning.” Neurocomputing 174: 988–998. doi:10.1016/j.neucom.2015.10.035.
   Web of Science ®Google Scholar
• Xiong, P., H. R. Wang, M. Liu, S. P. Zhou, Z. G. Hou, and X. L. Liu. 2016. “ECG Signal Enhancement Based on Improved Denoising Auto-Encoder.” Engineering Applications of Artificial Intelligence 52: 194–202. doi:10.1016/j.engappai.2016.02.015.
   Web of Science ®Google Scholar
• Yue, J., S. J. Mao, and M. Li. 2016. “A Deep Learning Framework for Hyperspectral Image Classification Using Spatial Pyramid Pooling.” Remote Sensing Letters 7 (9): 875–884. doi:10.1080/2150704X.2016.1193793.
   Web of Science ®Google Scholar
• Yue, J., W. Z. Zhao, S. J. Mao, and H. Liu. 2015. “Spectral-Spatial Classification of Hyperspectral Images Using Deep Convolutional Neural Networks.” Remote Sensing Letters 6 (6): 468–477. doi:10.1080/2150704X.2015.1047045.
   Web of Science ®Google Scholar
• Zhao, W. Z., and S. H. Du. 2016a. “Learning Multiscale and Deep Representations for Classifying Remotely Sensed Imagery.” Isprs Journal of Photogrammetry and Remote Sensing 113: 155–165. doi:10.1016/j.isprsjprs.2016.01.004.
   Web of Science ®Google Scholar
• Zhao, W. Z., and S. H. Du. 2016b. “Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach.” Ieee Transactions on Geoscience and Remote Sensing 54 (8): 4544–4554. doi:10.1109/Tgrs.2016.2543748.
   Web of Science ®Google Scholar