Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community

Journal of Applied Remote Sensing

Volumn 11, Issue 4, 2017, Pages

  Ball, John E ^a   Anderson, Derek T ^a   Chan, Chee Seng ^b  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

big data; computer vision; deep learning; hyperspectral; multispectral; remote sensing

Indexed keywords

BIG DATA; COMPUTER VISION; LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS; REMOTE SENSING; SPEECH RECOGNITION; SURVEYS;

HETEROGENEOUS DATA SOURCES; HYPERSPECTRAL; MULTI-SPECTRAL; NATURAL LANGUAGE PROCESSING; NEURAL NETWORKS (NNS); PHYSICAL PHENOMENA; STATE OF THE ART; TRANSFER LEARNING;

DEEP LEARNING;

EID: 85032865390     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.11.042609     Document Type: Review

References (419)

1. G. Cheng, J. Han, and X. Lu, "Remote sensing image scene classification: benchmark and state of the art," arXiv:1703.00121 [cs.CV] (2017).
2. L. Deng, "A tutorial survey of architectures, algorithms, and applications for deep learning," APSIPA Trans. Signal Inf. Process. 3(e2), 1-29 (2014).
3. L. Zhang, L. Zhang, and V. Kumar, "Deep learning for remote sensing data," IEEE Geosci. Remote Sens. Mag. 4, 22-40 (2016).
4. J. M. Bioucas-dias et al., "Hyperspectral remote sensing data analysis and future challenges," IEEE Geosci. Remote Sens. Mag. 1, 6-36 (2013).
5. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens. 43(6), 1351-1362 (2005).
6. G. Camps-Valls et al., "Advances in hyperspectral image classification: Earth monitoring with statistical learning methods," IEEE Signal Process. Mag. 31(1), 45-54 (2013).
7. H. Deborah, N. Richard, and J. Y. Hardeberg, "A comprehensive evaluation of spectral distance functions and metrics for hyperspectral image processing," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 3224-3234 (2015).
8. P. Dollar et al., "Pedestrian detection: an evaluation of the state of the art," IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743-761 (2012).
9. P. Du et al., "Multiple classifier system for remote sensing image classification: a review," Sensors, 12(4), 4764-4792 (2012).
10. M. Fauvel et al., "Advances in spectral-spatial classification of hyperspectral images," Proc. IEEE 101(3), 652-675 (2013).
11. M. Hussain et al., "Change detection from remotely sensed images: from pixel-based to object-based approaches," ISPRS J. Photogramm. Remote Sens. 80, 91-106 (2013).
12. G. Jianya et al., "A review of multi-temporal remote sensing data change detection algorithms," Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 37(B7), 757-762 (2008).
13. D. J. Lary et al., "Machine learning in geosciences and remote sensing," Geosci. Front. 7(1), 3-10 (2016).
14. D. Lunga et al., "Manifold-learning-based feature extraction for classification of hyperspectral data: a review of advances in manifold learning," IEEE Signal Process. Mag. 31(1), 55-66 (2014).
15. A. Plaza et al., "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens. 42(3), 650-663 (2004).
16. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag. 19(1), 44-57 (2002).
17. N. Keshava, "A survey of spectral unmixing algorithms," Lincoln Lab. J. 14(1), 55-78 (2003).
18. M. Parente and A. Plaza, "Survey of geometric and statistical unmixing algorithms for hyperspectral images," in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2010), pp. 1-4, IEEE (2010).
19. A. Plaza et al., "Recent developments in endmember extraction and spectral unmixing," in Optical Remote Sensing, S. Prasad, L. M. Bruce, and J. Chanussot, Eds., pp. 235-267, Springer, Berlin, Germany (2011).
20. C. Shi and L. Wang, "Incorporating spatial information in spectral unmixing: a review," Remote Sens. Environ. 149, 70-87 (2014).
21. M. D. Zeiler, G. W. Taylor, and R. Fergus, "Adaptive deconvolutional networks for mid and high level feature learning," in 2011 Int. Conf. on Computer Vision, pp. 2018-2025 (2011).
22. M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in European Conf. on Computer Vision, pp. 818-833, Springer (2014).
23. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, Massachusetts (2016).
24. D. Arpit et al., "Why regularized auto-encoders learn sparse representation?," in Int. Conf. on Machine Learning, pp. 136-144 (2016).
25. M. Chen et al., "Marginalized denoising autoencoders for domain adaptation," arXiv:1206.4683 (2012).
26. Ujjwalkarn, "An intuitive explanation of convolutional neural networks," https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ (2016).
27. Y. LeCun et al., "Gradient-based learning applied to document recognition," Proc. IEEE 86(11), 2278-2324 (1998).
28. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, G. Tesauro, D. Touretzky, and T. Leed, Eds., pp. 1097-1105 (2012).
29. C. Szegedy et al., "Going deeper with convolutions," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1-9 (2015).
30. K. He et al., "Deep residual learning for image recognition," CoRR abs/1512.03385 (2015).
31. G. Huang et al., "Densely connected convolutional networks," arXiv:1608.06993 (2016).
32. A. Deshpande, "A beginner's guide to understanding convolutional neural networks, part 2," http://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutionalneural-networks-part-2.html (29 July 2017).
33. A. Deshpande, "A beginner's guide to understanding convolutional neural networks, part 1," https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/ (29 July 2017).
34. V. Dumoulin and F. Visin, "A guide to convolution arithmetic for deep learning," arXiv:1603.07285 (2016).
35. G. Hinton and R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science 313(5786), 504-507 (2006).
36. R. Salakhutdinov and G. Hinton, "Deep Boltzmann machines," in Proc. of the Twelfth Int. Conf. on Artificial Intelligence and Statistics (AISTATS'09), Vol. 5, pp. 448-455 (2009).
37. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput. 9(8), 1735-1780 (1997).
38. D. Britz, "Recurrent neural networks tutorial, part 1 introduction to RNNs," http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ (29 July 2017).
39. "Recurrent neural networks," https://www.tensorflow.org/tutorials/recurrent (03 August 2017).
40. Q. V. Le, "A tutorial on deep learning part 2: autoencoders, convolutional neural networks and recurrent neural networks," Google Brain, pp. 1-20 (2015).
41. "A beginner's guide to recurrent networks and LSTMs," https://deeplearning4j.org/lstm.html (03 August 2017).
42. M. D. Zeiler et al., "Deconvolutional networks," in Computer Vision and Pattern Recognition (CVPR) (2010).
43. H. Noh, S. Hong, and B. Han, "Learning deconvolution network for semantic segmentation," CoRR abs/1505.04366 (2015).
44. O. Russakovsky et al., "ImageNet large scale visual recognition challenge," Int. J. Comput. Vision 115(3), 211-252 (2015).
45. L. Brown, "Deep learning with GPUs," www.nvidia.com/content/events/geoInt2015/LBrown-DL.pdf (2015).
46. G. Cybenko, "Approximation by superpositions of a sigmoidal function," Math. Control Signals Syst. 2(4), 303-314 (1989).
47. M. Telgarsky, "Benefits of depth in neural networks," arXiv:1602.04485 (2016).
48. O. Sharir and A. Shashua, "On the expressive power of overlapping operations of deep networks," arXiv:1703.02065 (2017).
49. S. Liang and R. Srikant, "Why deep neural networks for function approximation?," arXiv:1610.04161 (2016).
50. L. Zhang and P. N. Suganthan, "A survey of randomized algorithms for training neural networks," Inf. Sci. 364, 146-155 (2016).
51. T. Schaul, I. Antonoglou, and D. Silver, "Unit tests for stochastic optimization," arXiv:1312.6055 (2013).
52. B. T. Polyak, "Some methods of speeding up the convergence of iteration methods," USSR Comput. Math. Math. Phys. 4(5), 1-17 (1964).
53. J. Duchi, E. Hazan, and Y. Singer, "Adaptive subgradient methods for online learning and stochastic optimization," J. Mach. Learn. Res. 12, 2121-2159 (2011).
54. T. Tieleman and G. Hinton, "Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude," COURSERA: Neural Networks Mach. Learn. 4(2), 26-31 (2012).
55. D. Kingma and J. Ba, "Adam: a method for stochastic optimization," arXiv:1412.6980 (2014).
56. J. Schmidhuber, "Deep learning in neural networks: an overview," Neural Networks 61, 85-117 (2015).
57. G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput. 18(7), 1527-1554 (2006).
58. Y. Bengio et al., "Greedy layer-wise training of deep networks," Adv. Neural Inf. Process. Syst. 19, 153 (2007).
59. R. R. Salakhutdinov and G. E. Hinton, "A better way to pretrain deep Boltzmann machines," in Advances in Neural Information Processing Systems, F. Pereira et al., Eds., pp. 2447-2455, Curran Associates, Inc. (2012).
60. S. Shi et al., "Benchmarking state-of-the-art deep learning software tools," arXiv:1608.07249 (2016).
61. Y. Bengio, P. Simard, and P. Frasconi, "Learning long-term dependencies with gradient descent is difficult," IEEE Trans. Neural Networks 5(2), 157-166 (1994).
62. R. Pascanu, T. Mikolov, and Y. Bengio, "On the difficulty of training recurrent neural networks," in Int. Conf. on Machine Learning, pp. 1310-1318 (2013).
63. J. Martens and I. Sutskever, "Learning recurrent neural networks with Hessian-free optimization," in Proc. of the 28th Int. Conf. on Machine Learning (ICML 2011), pp. 1033-1040 (2011).
64. Y. Bengio, A. C. Courville, and P. Vincent, "Unsupervised feature learning and deep learning: a review and new perspectives," CoRR, abs/1206.55381 (2012).
65. Y. Ma et al., "Remote sensing big data computing: challenges and opportunities," Future Gener. Comput. Syst. 51, 47-60 (2015)Special section: a note on new trends in data-aware scheduling and resource provisioning in modern HPC systems.
66. M. Chi et al., "Big data for remote sensing: challenges and opportunities," Proc. IEEE 104, 2207-2219 (2016).
67. R. Raina, A. Madhavan, and A. Y. Ng, "Large-scale deep unsupervised learning using graphics processors," in Proc. of the 26th Annual Int. Conf. on Machine Learning (ICML 2009), pp. 873-880, ACM, New York (2009).
68. D. C. Cireşan et al., "Flexible, high performance convolutional neural networks for image classification," in Proc. of the 22nd Int. Joint Conf. on Artificial Intelligence (IJCAI 2011), Vol. 2, pp. 1237-1242, AAAI Press (2011).
69. D. Scherer, A. Müller, and S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition," in Proc. of the 20th Int. Conf. on Artificial Neural Networks: Part III (ICANN 2010), pp. 92-101, Springer-Verlag, Berlin, Heidelberg (2010).
70. L. Deng, D. Yu, and J. Platt, "Scalable stacking and learning for building deep architectures," in 2012 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 2133-2136 (2012).
71. B. Hutchinson, L. Deng, and D. Yu, "Tensor deep stacking networks," IEEE Trans. Pattern Anal. Mach. Intell. 35, 1944-1957 (2013).
72. J. Dean et al., "Large scale distributed deep networks," in Proc. of the 25th Int. Conf. on Neural Information Processing Systems (NIPS 2012), pp. 1223-1231, Curran Associates Inc., USA (2012).
73. R. K. Srivastava, K. Greff, and J. Schmidhuber, "Highway networks," CoRR abs/1505.00387 (2015).
74. K. Greff, R. K. Srivastava, and J. Schmidhuber, "Highway and residual networks learn unrolled iterative estimation," CoRR abs/1612.07771 (2016).
75. J. G. Zilly et al., "Recurrent highway networks," CoRR abs/1607.03474 (2016).
76. R. K. Srivastava, K. Greff, and J. Schmidhuber, "Training very deep networks," CoRR abs/1507.06228 (2015).
77. M. M. Najafabadi et al., "Deep learning applications and challenges in big data analytics," J. Big Data 2(1), 1-21 (2015).
78. X.-W. Chen and X. Len, "Big data deep learning: challenges and perspectives," IEEE Access 2, 514-525 (2014).
79. S. Landset et al., "A survey of open source tools for machine learning with big data in the Hadoop ecosystem," J. Big Data 2(1), 24 (2015).
80. J. Wan et al., "Deep learning for content-based image retrieval: a comprehensive study," in Proc. of the 22nd ACM Int. Conf. on Multimedia, pp. 157-166 (2014).
81. Y. Jia et al., "Caffe: convolutional architecture for fast feature embedding," in ACM Int. Conf. on Multimedia, pp. 675-678 (2014).
82. M. Abadi et al., "TensorFlow: a system for large-scale machine learning," in Proc. of the 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI), Savannah, Georgia (2016).
83. A. Vedaldi and K. Lenc, "MatConvNet: convolutional neural networks for MATLAB," in Proc. of the 23rd ACM Int. Conf. on Multimedia, pp. 689-692, ACM (2015).
84. A. Handa et al., "GVNN: neural network library for geometric computer vision," in Computer Vision Workshop (ECCV 2016), pp. 67-82, Springer International (2016).
85. F. Chollet, "Keras," https://github.com/fchollet/keras (2015).
86. T. Chen et al., "MXNet: a flexible and efficient machine learning library for heterogeneous distributed systems," 1-6, arXiv:1512.01274 (2015).
87. R. Al-Rfou et al., "Theano: a Python framework for fast computation of mathematical expressions," arXiv:1605.02688 (2016).
88. "Deep learning with torch: the 60-minute Blitz," https://github.com/soumith/cvpr2015/blob/master/Deep Learning with Torchipynb (18 February 2017).
89. M. Baccouche et al., "Sequential deep learning for human action recognition," in Int. Workshop on Human Behavior Understanding, pp. 29-39, Springer (2011).
90. Z. Shou et al., "CDC: convolutional-de-convolutional networks for precise temporal action localization in untrimmed videos," arXiv:1703.01515 (2017).
91. D. Tran et al., "Learning spatiotemporal features with 3D convolutional networks," in IEEE Int. Conf. on Computer Vision (ICCV 2015), pp. 4489-4497 (2015).
92. L. Wang et al., "Temporal segment networks: towards good practices for deep action recognition," in European Conf. on Computer Vision, pp. 20-36 (2016).
93. G. E. Dahl et al., "Context-dependent pre-trained deep neural networks for largevocabulary speech recognition," IEEE Trans. Audio Speech Lang. Process. 20(1), 30-42 (2012).
94. G. Hinton et al., "Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups," IEEE Signal Process. Mag. 29(6), 82-97 (2012).
95. A. Graves, A.-R. Mohamed, and G. Hinton, "Speech recognition with deep recurrent neural networks," in IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2013), pp. 6645-6649, IEEE (2013).
96. W. Luo, A. G. Schwing, and R. Urtasun, "Efficient deep learning for stereo matching," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 5695-5703 (2016).
97. S. Levine et al., "Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection," arXiv:1603.02199 (2016).
98. S. Venugopalan et al., "Sequence to sequence-video to text," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 4534-4542 (2015).
99. R. Collobert, "Deep learning for efficient discriminative parsing," in Proc. of the Fourteenth Int. Conf. on Artificial Intelligence and Statistics, Vol. 15, pp. 224-232 (2011).
100. S. Venugopalan et al., "Translating videos to natural language using deep recurrent neural networks," arXiv:1412.4729 (2014).
101. Y. H. Tan and C. S. Chan, "phi-LSTM: a phrase-based hierarchical LSTM model for image captioning," in 13th Asian Conf. on Computer Vision (ACCV), pp. 101-117 (2016).
102. A. Karpathy and L. Fei-Fei, "Deep visual-semantic alignments for generating image descriptions," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 3128-3137 (2015).
103. P. Baldi, P. Sadowski, and D. Whiteson, "Searching for exotic particles in high-energy physics with deep learning," Nat. Commun. 5, 4308 (2014).
104. J. Wu et al., "Galileo: perceiving physical object properties by integrating a physics engine with deep learning," in Advances in Neural Information Processing Systems, C. Cortes et al., Eds., pp. 127-135, Curran Associates, Inc. (2015).
105. A. A. Cruz-Roa et al., "A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection," in Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, pp. 403-410, Springer (2013).
106. R. Fakoor et al., "Using deep learning to enhance cancer diagnosis and classification," in Proc. of the Int. Conf. on Machine Learning (2013).
107. K. Sirinukunwattana et al., "Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images," IEEE Trans. Med. Imaging 35(5), 1196-1206 (2016).
108. M. Längkvist, L. Karlsson, and A. Loutfi, "A review of unsupervised feature learning and deep learning for time-series modeling," Pattern Recognit. Lett. 42, 11-24 (2014).
109. S. Sarkar et al., "Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis," in Annual Conf. of the Prognostics and Health Management (2015).
110. T. Kuremoto et al., "Time series forecasting using a deep belief network with restricted Boltzmann machines," Neurocomputing 137, 47-56 (2014).
111. A. Dosovitskiy, J. Tobias Springenberg, and T. Brox, "Learning to generate chairs with convolutional neural networks," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1538-1546 (2015).
112. J. Zhao, M. Mathieu, and Y. LeCun, "Energy-based generative adversarial network," arXiv:1609.03126 (2016).
113. S. E. Reed et al., "Learning what and where to draw," in Advances in Neural Information Processing Systems, D. D. Lee et al., Eds., pp. 217-225, Curran Associates, Inc. (2016).
114. D. Berthelot, T. Schumm, and L. Metz, "Began: boundary equilibrium generative adversarial networks," arXiv:1703.10717 (2017).
115. W. R. Tan et al., "ArtGAN: artwork synthesis with conditional categorial gans," arXiv:1702.03410 (2017).
116. K. Gregor et al., "Draw: a recurrent neural network for image generation," arXiv:1502.04623 (2015).
117. A. Radford, L. Metz, and S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks," arXiv:1511.06434 (2015).
118. X. Ding et al., "Deep learning for event-driven stock prediction," in Proc. of the Twenty-Fourth Int. Joint Conf. on Artificial Intelligence (IJCAI '15), pp. 2327-2333 (2015).
119. Z. Yuan et al., "Droid-sec: deep learning in android malware detection," ACM SIGCOMM Comput. Commun. Rev. 44(4), 371-372 (2014).
120. I. Arel, D. C. Rose, and T. P. Karnowski, "Deep machine learning-a new frontier in artificial intelligence research," IEEE Comput. Intell. Mag. 5(4), 13-18 (2010).
121. W. Liu et al., "A survey of deep neural network architectures and their applications," Neurocomputing 234, 11-26 (2017).
122. H. Wang and B. Raj, "A survey: time travel in deep learning space: an introduction to deep learning models and how deep learning models evolved from the initial ideas," arXiv:1510.04781 abs/1510.04781 (2015).
123. D. Yu and L. Deng, "Deep learning and its applications to signal and information processing [exploratory DSP]," IEEE Signal Process. Mag. 28(1), 145-154 (2011).
124. L. Arnold et al., "An introduction to deep learning," in 11th Int. Conf. on Information Science, Signal Processing and their Applications (ISSPA '12), pp. 1438-1439 (2012).
125. L. Deng and D. Yu, "Deep learning: methods and applications," Found. Trends Signal Process. 7(3-4), 197-387 (2013).
126. C. Cadena, A. Dick, and I. D. Reid, "Multi-modal auto-encoders as joint estimators for robotics scene understanding," in Proc. of Robotics: Science and Systems Conf. (RSS) (2016).
127. J. Feng, Y. Wang, and S.-F. Chang, "3D shape retrieval using a single depth image from low-cost sensors," in IEEE Winter Conf. on Applications of Computer Vision (WACV '16), pp. 1-9 (2016).
128. A. Haque, A. Alahi, and L. Fei-fei, "Recurrent attention models for depth-based person identification," in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR '16), pp. 1229-1238 (2016).
129. V. Hegde and R. Zadeh, "FusionNet: 3D object classification using multiple data representations," arXiv:1607.05695 (2016).
130. J. Huang and S. You, "Point cloud labeling using 3D convolutional neural network," in Int. Conf. on Pattern Recognition (2016).
131. W. Kehl et al., "Deep learning of local RGB-D patches for 3D object detection and 6D pose estimation," in European Conf. on Computer Vision, pp. 205-220 (2016).
132. C. Li, A. Reiter, and G. D. Hager, "Beyond spatial pooling: fine-grained representation learning in multiple domains," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 2, pp. 4913-4922 (2015).
133. N. Sedaghat, M. Zolfaghari, and T. Brox, "Orientation-boosted voxel nets for 3D object recognition," arXiv:1604.03351 (2016).
134. Z. Xie et al., "Projective feature learning for 3D shapes with multi-view depth images," in Computer Graphics Forum, Vol. 34(7), pp. 1-11, Wiley Online Library (2015).
135. C. Chen, "DeepDriving: learning affordance for direct perception in autonomous driving," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 2722-2730 (2015).
136. X. Chen et al., "Multi-view 3D object detection network for autonomous driving," arXiv:1611.07759 (2016).
137. A. Chigorin and A. Konushin, "A system for large-scale automatic traffic sign recognition and mapping," in CMRT13-City Models, Roads and Traffic 2013, pp. 13-17 (2013).
138. D. Ciresan, U. Meier, and J. Schmidhuber, "Multi-column deep neural networks for image classification," in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR '12), pp. 3642-3649 (2012).
139. Y. Zeng et al., "Traffic sign recognition using extreme learning classifier with deep convolutional features," in Int. Conf. on Intelligence Science and Big Data Engineering (IScIDE '15), Suzhou, China (2015).
140. A.-B. Salberg, "Detection of seals in remote sensing images using features extracted from deep convolutional neural networks," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS'15), pp. 1893-1896 (2015).
141. W. Li, G. Wu, and Q. Du, "Transferred deep learning for anomaly detection in hyperspectral imagery," IEEE Geosci. Remote Sens. Lett. 14(5), 597-601 (2017).
142. J. Becker et al., "Deep belief networks for false alarm rejection in forward-looking groundpenetrating radar," Proc. SPIE 9454, 94540W (2015).
143. C. Bentes, D. Velotto, and S. Lehner, "Target classification in oceanographic SAR images with deep neural networks: architecture and initial results," in IEEE Int. Geoscience and Remote Sensing Symposium (IGARSS '15), pp. 3703-3706, IEEE (2015).
144. L. E. Besaw, "Detecting buried explosive hazards with handheld GPR and deep learning," Proc. SPIE 9823, 98230N (2016).
145. L. E. Besaw and P. J. Stimac, "Deep learning algorithms for detecting explosive hazards in ground penetrating radar data," Proc. SPIE 9072, 90720Y (2014).
146. K. Du et al., "SAR ATR based on displacement-and rotation-insensitive CNN," Remote Sens. Lett. 7(9), 895-904 (2016).
147. S. Chen and H. Wang, "SAR target recognition based on deep learning," in Int. Conf. on Data Science and Advanced Analytics (DSAA '14), pp. 541-547, IEEE (2014).
148. D. A. E. Morgan, "Deep convolutional neural networks for ATR from SAR imagery," Proc. SPIE 9475, 94750F (2015).
149. J. C. Ni and Y. L. Xu, "SAR automatic target recognition based on a visual cortical system," in 6th Int. Congress on Image and Signal Processing (CISP '13), Vol. 2, pp. 778-782, IEEE (2013).
150. Z. Sun, L. Xue, and Y. Xu, "Recognition of SAR target based on multilayer auto-encoder and SNN," Int. J. Innovative Comput. Inf. Control 9(11), 4331-4341 (2013).
151. H. Wang et al., "Application of deep-learning algorithms to MSTAR data," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS '15), pp. 3743-3745, IEEE (2015).
152. L. Zhang et al., "A multifeature tensor for remote-sensing target recognition," IEEE Geosci. Remote Sens. Lett. 8(2), 374-378 (2011).
153. L. Zhang, Z. Shi, and J. Wu, "A hierarchical oil tank detector with deep surrounding features for high-resolution optical satellite imagery," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(10), 4895-4909 (2015).
154. P. F. Alcantarilla et al., "Streetview change detection with deconvolutional networks," in Robotics: Science and Systems Conf. (RSS) (2016).
155. M. Gong, Z. Zhou, and J. Ma, "Change detection in synthetic aperture radar images based on deep neural networks," IEEE Trans. Neural Networks Learn. Syst. 27(1), 125-138 (2016).
156. F. Pacifici et al., "An innovative neural-net method to detect temporal changes in highresolution optical satellite imagery," IEEE Trans. Geosci. Remote Sens. 45(9), 2940-2952 (2007).
157. S. Stent, "Detecting change for multi-view, long-term surface inspection," in Proc. of the 2015 British Machine Vision Conf. (BCMV '15), pp. 1-12 (2015).
158. J. Zhao et al., "Deep learning to classify difference image for image change detection," in Int. Joint Conf. on Neural Networks (IJCNN '14), pp. 411-417, IEEE (2014).
159. S. Basu et al., "DeepSat: a learning framework for satellite imagery," in Proc. of the 23rd SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, pp. 1-22 (2015).
160. Y. Bazi et al., "Differential evolution extreme learning machine for the classification of hyperspectral images," IEEE Trans. Geosci. Remote Sens. 11(6), 1066-1070 (2014).
161. J. Cao, Z. Chen, and B. Wang, "Deep convolutional networks with superpixel segmentation for hyperspectral image classification," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 3310-3313 (2016).
162. J. Cao, Z. Chen, and B. Wang, "Graph-based deep convolutional networks with superpixel segmentation for hyperspectral image classification," in 2016 IEEE Geoscience and Remote Sensing Symposium (IGARSS), pp. 3310-3313 (2016).
163. C. Chen, W. Li, and H. Su et al., "Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine," Remote Sens. 6(6), 5795-5814 (2014).
164. G. Cheng et al., "Scene classification of high resolution remote sensing images using convolutional neural networks," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 767-770 (2016).
165. F. Del Frate et al., "Use of neural networks for automatic classification from high-resolution images," IEEE Trans. Geosci. Remote Sens. 45(4), 800-809 (2007).
166. Z. Fang, W. Li, and Q. Du, "Using CNN-based high-level features for remote sensing scene classification," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 2610-2613 (2016).
167. Q. Fu et al., "Semi-supervised classification of hyperspectral imagery based on stacked autoencoders," Proc. SPIE 10033, 100332B (2016).
168. J. Geng et al., "High-resolution SAR image classification via deep convolutional autoencoders," IEEE Trans. Geosci. Remote Sens. Lett. 12(11), 2351-2355 (2015).
169. X. Han et al., "Scene classification based on a hierarchical convolutional sparse autoencoder for high spatial resolution imagery," Int. J. Remote Sens. 38(2), 514-536 (2017).
170. M. He et al., "Hyperspectral image classification based on deep stacking network," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 3286-3289 (2016).
171. B. Hou et al., "Polarimetric SAR images classification using deep belief networks with learning features," in IEEE Int. Geoscience and Remote Sensing Symposium (IGARSS), pp. 2366-2369 (2015).
172. W. Hu et al., "Deep convolutional neural networks for hyperspectral image classification," J. Sens. 2015, 1-12 (2015).
173. M. Iftene, Q. Liu, and Y. Wang, "Very high resolution images classification by fine tuning deep convolutional neural networks," Proc. SPIE 10033, 100332D (2016).
174. P. Jia et al., "Convolutional neural network based classification for hyperspectral data," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 5075-5078 (2016).
175. P. Kontschieder et al., "Deep neural decision forests," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 1467-1475 (2015).
176. M. Längkvist et al., "Classification and segmentation of satellite orthoimagery using convolutional neural networks," Remote Sens. 8(4), 329 (2016).
177. H. Lee and H. Kwon, "Contextual deep CNN based hyperspectral classification," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 3322-3325 (2016).
178. J. Li, "Active learning for hyperspectral image classification with a stacked autoencoders based neural network," in IEEE Int. Conf. on Image Processing (ICIP '16), pp. 1062-1065 (2016).
179. J. Li, L. Bruzzone, and S. Liu, "Deep feature representation for hyperspectral image classification," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS '15), pp. 4951-4954 (2015).
180. T. Li, J. Zhang, and Y. Zhang, "Classification of hyperspectral image based on deep belief networks," in IEEE Int. Conf. on Image Processing (ICIP '14), pp. 5132-5136 (2014).
181. W. Li et al., "Hyperspectral image classification using deep pixel-pair features," IEEE Trans. Geosci. Remote Sens. 55(2), 844-853 (2017).
182. Y. Li, W. Xie, and H. Li, "Hyperspectral image reconstruction by deep convolutional neural network for classification," Pattern Recognit. 63, 371-383 (2017).
183. Z. Lin et al., "Spectral-spatial classification of hyperspectral image using autoencoders," in 9th Int. Conf. on Information, Communications and Signal Processing (ICICS '13), pp. 1-5, IEEE (2013).
184. Y. Liu et al., "Hyperspectral classification via deep networks and superpixel segmentation," Int. J. Remote Sens. 36(13), 3459-3482 (2015).
185. Y. Liu et al., "Hyperspectral classification via learnt features," in Int. Conf. on Image Processing (ICIP '15), pp. 1-5 (2015).
186. P. Liu, H. Zhang, and K. B. Eom, "Active deep learning for classification of hyperspectral images," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(2), 712-724 (2017).
187. X. Ma, H. Wang, and J. Geng, "Spectral-spatial classification of hyperspectral image based on deep auto-encoder," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(9), 4073-4085 (2016).
188. X. Ma et al., "Hyperspectral image classification with small training set by deep network and relative distance prior," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 3282-3285, IEEE (2016).
189. X. Mei et al., "Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints," Int. J. Remote Sens. 36(18), 4724-4747 (2015).
190. S. Mei et al., "Integrating spectral and spatial information into deep convolutional neural networks for hyperspectral classification," in IEEE Geoscience and Remote Sensing Symp. (IGARSS'15), pp. 5067-5070 (2016).
191. A. Merentitis and C. Debes, "Automatic fusion and classification using random forests and features extracted with deep learning," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS '15), pp. 2943-2946 (2015).
192. K. Nogueira, O. A. B. Penatti, and J. A. dos Santos, "Towards better exploiting convolutional neural networks for remote sensing scene classification," Pattern Recognit. 61, 539-556 (2017).
193. B. Pan, Z. Shi, and X. Xu, "R-VCANet: a new deep-learning-based hyperspectral image classification method," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(5), 1975-1986 (2017).
194. M. Papadomanolaki et al., "Benchmarking deep learning frameworks for the classification of very high resolution satellite multispectral data," in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 3, no. 7, pp. 83-88 (2016).
195. S. Piramanayagam et al., "Classification of remote sensed images using random forests and deep learning framework," Proc. SPIE 10004, 100040L (2016).
196. F. Qin, J. Guo, and W. Sun, "Object-oriented ensemble classification for polarimetric SAR imagery using restricted Boltzmann machines," Remote Sens. Lett. 8(3), 204-213 (2017).
197. S. Rajan, J. Ghosh, and M. M. Crawford, "An active learning approach to hyperspectral data classification," IEEE Trans. Geosci. Remote Sens. 46(4), 1231-1242 (2008).
198. Z. Wang, N. M. Nasrabadi, and T. S. Huang, "Semisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization," IEEE Trans. Geosci. Remote Sens. 53(3), 1161-1173 (2015).
199. J. Yang et al., "Hyperspectral image classification using two-channel deep convolutional neural network," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 5079-5082 (2016).
200. S. Yu, S. Jia, and C. Xu, "Convolutional neural networks for hyperspectral image classification," Neurocomputing 219, 88-98 (2017).
201. J. Yue, S. Mao, and M. Li, "A deep learning framework for hyperspectral image classification using spatial pyramid pooling," Remote Sens. Lett. 7(9), 875-884 (2016).
202. J. Yue et al., "Spectral-spatial classification of hyperspectral images using deep convolutional neural networks," Remote Sens. Lett. 6(6), 468-477 (2015).
203. A. Zeggada and F. Melgani, "Multilabel classification of UAV images with convolutional neural networks," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 5083-5086, IEEE (2016).
204. F. Zhang, B. Du, and L. Zhang, "Scene classification via a gradient boosting randomconvolutional network framework," IEEE Trans. Geosci. Remote Sens. 54(3), 1793-1802 (2016).
205. H. Zhang et al., "Spectral-spatial classification of hyperspectral imagery using a dualchannel convolutional neural network," Remote Sens. Lett. 8(5), 438-447 (2017).
206. W. Zhao and S. Du, "Learning multiscale and deep representations for classifying remotely sensed imagery," ISPRS J. Photogramm. Remote Sens. 113, 155-165 (2016).
207. Y. Zhong, F. Fei, and L. Zhang, "Large patch convolutional neural networks for the scene classification of high spatial resolution imagery," J. Appl. Remote Sens. 10(2), 025006 (2016).
208. Y. Zhong et al., "SatCNN: satellite image dataset classification using agile convolutional neural networks," Remote Sens. Lett. 8(2), 136-145 (2017).
209. Y. Chen et al., "Deep fusion of hyperspectral and LiDAR data for thematic classification," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 3591-3594 (2016).
210. L. Ran et al., "Bands sensitive convolutional network for hyperspectral image classification," in Proc. of the Int. Conf. on Internet Multimedia Computing and Service, pp. 268-272, ACM (2016).
211. J. Zabalza et al., "Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging," Neurocomputing 185, 1-10 (2016).
212. Y. Liu and L. Wu, "Geological disaster recognition on optical remote sensing images using deep learning," Procedia Comput. Sci. 91, 566-575 (2016).
213. J. Chen, Q. Jin, and J. Chao, "Design of deep belief networks for short-term prediction of drought index using data in the Huaihe river basin," Math. Prob. Eng. 2012, 235929 (2012).
214. P. Landschützer et al., "A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink," Biogeosciences 10(11), 7793-7815 (2013).
215. M. Shi et al., "Cloud detection of remote sensing images by deep learning," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 701-704, IEEE (2016).
216. X. Shi et al., "Convolutional LSTM network: a machine learning approach for precipitation nowcasting," in Advances in Neural Information Processing Systems, C. Cortes et al., Eds., pp. 802-810, Curran Associates, Inc. (2015).
217. S. Lee, H. Zhang, and D. J. Crandall, "Predicting geo-informative attributes in large-scale image collections using convolutional neural networks," in IEEE Winter Conf. on Applications of Computer Vision (WACV '15), pp. 550-557 (2015).
218. Y. Kim and T. Moon, "Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks," IEEE Geosci. Remote Sens. Lett. 13(1), 1-5 (2015).
219. W. Ouyang and X. Wang, "A discriminative deep model for pedestrian detection with occlusion handling," in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR '12), pp. 3258-3265, IEEE (2012).
220. D. Tomè et al., "Deep convolutional neural networks for pedestrian detection," Signal Process. Image Commun. 47, 482-489 (2016).
221. Y. Wei et al., "A universal remote sensing image quality improvement method with deep learning," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 6950-6953 (2016).
222. H. Zhang et al., "Systematic infrared image quality improvement using deep learning based techniques," Proc. SPIE 10008, 100080P (2016).
223. D. Quan et al., "Using deep neural networks for synthetic aperture radar image registration," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 2799-2802 (2016).
224. P. Ghamisi, Y. Chen, and X. X. Zhu, "A self-improving convolution neural network for the classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett. 13(10), 1537-1541 (2016).
225. N. Kussul et al., "Deep learning approach for large scale land cover mapping based on remote sensing data fusion," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 198-201, IEEE (2016).
226. W. Li et al., "Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping," Int. J. Remote Sens. 37(23), 5632-5646 (2016).
227. H. Liu et al., "Terrain classification with polarimetric SAR based on deep sparse filtering network," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 64-67 (2016).
228. K. Makantasis et al., "Deep supervised learning for hyper-spectral data classification through convolutional neural networks," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), pp. 4959-4962 (2015).
229. M. Castelluccio et al., "Land use classification in remote sensing images by convolutional neural networks," 1-11, arXiv:1508.00092 (2015).
230. G. Cheng et al., "Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images," IEEE Trans. Geosci. Remote Sens. 53(8), 4238-4249 (2015).
231. F. P. S. Luus et al., "Multiview deep learning for land-use classification," IEEE Geosci. Remote Sens. Lett. 12(12), 2448-2452 (2015).
232. Q. Lv et al., "Urban land use and land cover classification using remotely sensed SAR data through deep belief networks," J. Sens. 2015, 1-10 (2015).
233. X. Ma, H. Wang, and J. Wang, "Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning," ISPRS J. Photogramm. Remote Sens. 120, 99-107 (2016).
234. M. E. Midhun et al., "Deep model for classification of hyperspectral image using restricted Boltzmann machine," in Proc. of the 2014 Int. Conf. on Interdisciplinary Advances in Applied Computing (ICONIAAC '14), pp. 1-7 (2014).
235. E. Othman et al., "Using convolutional features and a sparse autoencoder for land-use scene classification," Int. J. Remote Sens. 37(10), 2149-2167 (2016).
236. A. B. Penatti, K. Nogueira, and J. A. Santos, "Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?" in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, pp. 44-51 (2015).
237. A. Romero, C. Gatta, and G. Camps-Valls, "Unsupervised deep feature extraction for remote sensing image classification," IEEE Trans. Geosci. Remote Sens. 54(3), 1349-1362 (2016).
238. Y. Sun et al., "Active learning based autoencoder for hyperspectral imagery classification," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 469-472 (2016).
239. N. K. Uba, "Land use and land cover classification using deep learning techniques," PhD Thesis, Arizona State University (2016).
240. L. Alexandre, "3D object recognition using convolutional neural networks with transfer learning between input channels," in 13th Int. Conf. on Intelligent Autonomous Systems, pp. 889-898, Springer International (2016).
241. X. Chen et al., "Aircraft detection by deep belief nets," in Proc. 2nd IAPR Asian Conf. on Pattern Recognition (ACPR '13), pp. 54-58 (2013).
242. X. Chen and Y. Zhu, "3D object proposals for accurate object class detection," in Advances in Neural Information Processing Systems, C. Cortes et al., Eds., pp. 424-432, Curran Associates, Inc. (2015).
243. G. Cheng, P. Zhou, and J. Han, "Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images," IEEE Trans. Geosci. Remote Sens. 54(12), 7405-7415 (2016).
244. M. Dahmane et al., "Object detection in pleiades images using deep features," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 1552-1555 (2016).
245. W. Diao et al., "Object recognition in remote sensing images using sparse deep belief networks," Remote Sens. Lett. 6(10), 745-754 (2015).
246. G. Georgakis et al., "Multiview RGB-D dataset for object instance detection," in IEEE Fourth Int. Conf. on 3D Vision (3DV '16), pp. 426-434 (2016).
247. D. Maturana and S. Scherer, "3D convolutional neural networks for landing zone detection from LiDAR," in IEEE Int. Conf. on Robotics and Automation (ICRA '15), pp. 3471-3478 (2015).
248. C. Wang and K. Siddiqi, "Differential geometry boosts convolutional neural networks for object detection," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, pp. 51-58 (2016).
249. Q. Wu et al., "Shape-based object extraction in high-resolution remote-sensing images using deep Boltzmann machine," Int. J. Remote Sens. 37(24), 6012-6022 (2016).
250. B. Zhou et al., "Object detectors emerge in deep scene CNNs," http://hdl.handle.net/1721./96942 (2015).
251. P. Ondruska and I. Posner, "Deep tracking: seeing beyond seeing using recurrent neural networks," in Proc. of the 30th Conf. on Artificial Intelligence (AAAI '16), pp. 3361-3367 (2016).
252. G. Masi et al., "Pansharpening by convolutional neural networks," Remote Sens. 8(7), 594 (2016).
253. W. Huang et al., "A new pan-sharpening method with deep neural networks," IEEE Geosci. Remote Sens. Lett. 12(5), 1037-1041 (2015).
254. L. Palafox, A. Alvarez, and C. Hamilton, "Automated detection of impact craters and volcanic rootless cones in mars satellite imagery using convolutional neural networks and support vector machines," in 46th Lunar and Planetary Science Conf., pp. 1-2 (2015).
255. M. M. Ghazi, B. Yanikoglu, and E. Aptoula, "Plant identification using deep neural networks via optimization of transfer learning parameters," Neurocomputing 235, 228-235 (2017).
256. H. Guan et al., "Deep learning-based tree classification using mobile LiDAR data," Remote Sens. Lett. 6(11), 864-873 (2015).
257. P. K. Goel et al., "Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn," Comput. Electron. Agric. 39(2), 67-93 (2003).
258. K. Kuwata and R. Shibasaki, "Estimating crop yields with deep learning and remotely sensed data," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS '15), pp. 858-861 (2015).
259. J. Rebetez et al., "Augmenting a convolutional neural network with local histograms-a case study in crop classification from high-resolution UAV imagery," in European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 515-520 (2016).
260. S. Sladojevic et al., "Deep neural networks based recognition of plant diseases by leaf image classification," Comput. Intell. Neurosci. 2016, 1-11 (2016).
261. D. Levi, N. Garnett, and E. Fetaya, "StixelNet: a deep convolutional network for obstacle detection and road segmentation," in Proc. of the British Machine Vision Conf. (BMVC '15) (2015).
262. P. Li et al., "Road network extraction via deep learning and line integral convolution," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 1599-1602, IEEE (2016).
263. V. Mnih and G. Hinton, "Learning to label aerial images from noisy data," in Proc. of the 29th Int. Conf. on Machine Learning (ICML-12), pp. 567-574 (2012).
264. J. Wang et al., "Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine," Int. J. Remote Sens. 36(12), 3144-3169 (2015).
265. Y. Yu et al., "Automated extraction of 3D trees from mobile LiDAR point clouds," in ISPRS Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 40, No. 5, pp. 629-632 (2014).
266. Y. Yu, H. Guan, and Z. Ji, "Automated extraction of urban road manhole covers using mobile laser scanning data," IEEE Trans. Intell. Transp. Syst. 16(4), 2167-2181 (2015).
267. Z. Zhong et al., "Fully convolutional networks for building and road extraction: preliminary results," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 1591-1594 (2016).
268. R. Hadsell et al., "Learning long-range vision for autonomous off-road driving," J. Field Rob. 26(2), 120-144 (2009).
269. L. Mou and X. X. Zhu, "Spatiotemporal scene interpretation of space videos via deep neural network and tracklet analysis," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 1823-1826, IEEE (2016).
270. Y. Yuan, L. Mou, and X. Lu, "Scene recognition by manifold regularized deep learning architecture," IEEE Trans. Neural Networks Learn. Syst. 26(10), 2222-2233 (2015).
271. C. Couprie et al., "Indoor semantic segmentation using depth information," 1-8, arXiv:1301.3572 (2013).
272. Y. Gong et al., "Deep convolutional ranking for multilabel image annotation," 1-9, arXiv:1312.4894 (2013).
273. M. Kampffmeyer, A.-B. Salberg, and R. Jenssen, "Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, pp. 1-9 (2016).
274. P. Kaiser, "Learning city structures from online maps," Master's Thesis, ETH Zurich (2016).
275. A. Lagrange et al., "Benchmarking classification of earth-observation data: from learning explicit features to convolutional networks," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS '15), pp. 4173-4176 (2015).
276. D. Marmanis et al., "Deep learning earth observation classification using ImageNet pretrained networks," IEEE Geosci. Remote Sens. Lett. 13(1), 105-109 (2016).
277. D. Marmanis et al., "Semantic segmentation of aerial images with an ensemble of CNNs," in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 3, pp. 473-480 (2016).
278. S. Paisitkriangkrai et al., "Effective semantic pixel labelling with convolutional networks and conditional random fields," in IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, pp. 36-43 (2015).
279. B. Qu et al., "Deep semantic understanding of high resolution remote sensing image," in Int. Conf. on Computer, Information and Telecommunication Systems (CITS '16), pp. 1-5 (2016).
280. J. Sherrah, "Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery," arXiv:1606.02585 (2016).
281. C. Vaduva, I. Gavat, and M. Datcu, "Deep learning in very high resolution remote sensing image information mining communication concept," in Proc. of the 20th European Signal Processing Conf. (EUSIPCO '12), pp. 2506-2510, IEEE (2012).
282. M. Volpi and D. Tuia, "Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks," IEEE Trans. Geosci. Remote Sens. 55(2), 881-893 (2017).
283. D. Zhang et al., "Co-saliency detection via looking deep and wide," in Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 2994-3002 (2015).
284. F. I. Alam et al., "CRF learning with CNN features for hyperspectral image segmentation," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 6890-6893 (2016).
285. N. Audebert, B. L. Saux, and S. Lefèvre, "How useful is region-based classification of remote sensing images in a deep learning framework?" in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 5091-5094 (2016).
286. E. Basaeed et al., "A supervised hierarchical segmentation of remote-sensing images using a committee of multi-scale convolutional neural networks," Int. J. Remote Sens. 37(7), 1671-1691 (2016).
287. E. Basaeed, H. Bhaskar, and M. Al-Mualla, "Supervised remote sensing image segmentation using boosted convolutional neural networks," Knowl. Based Syst. 99, 19-27 (2016).
288. S. Pal, S. Chowdhury, and S. K. Ghosh, "DCAP: a deep convolution architecture for prediction of urban growth," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 1812-1815, IEEE (2016).
289. J. Wang et al., "Deep hierarchical representation and segmentation of high resolution remote sensing images," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS '15), pp. 4320-4323 (2015).
290. C. P. Schwegmann et al., "Very deep learning for ship discrimination in synthetic aperture radar imagery," in IEEE Geoscience and Remote Sensing Symp. (IGARSS '16), pp. 104-107, IEEE (2016).
291. J. Tang et al., "Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine," IEEE Trans. Geosci. Remote Sens. 53(3), 1174-1185 (2015).
292. R. Zhang et al., "S-CNN-based ship detection from high-resolution remote sensing images," in Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLI-B7, pp. 423-430 (2016).
293. Z. Cui et al., "Deep network cascade for image super-resolution," Lect. Notes Comput. Sci. 8693, 49-64 (2014).
294. C. Dong et al., "Image super-resolution using deep convolutional networks," IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295-307 (2016).
295. A. Ducournau and R. Fablet, "Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data," in 9th Workshop on Pattern Recognition in Remote Sensing (2016).
296. L. Liebel and M. Körner, "Single-image super resolution for multispectral remote sensing data using convolutional neural networks," in XXIII ISPRS Congress Proc., pp. 883-890 (2016).
297. W. Huang et al., "Deep architecture for traffic flow prediction: deep belief networks with multitask learning," IEEE Trans. Intell. Transp. Syst. 15(5), 2191-2201 (2014).
298. Y. Lv et al., "Traffic flow prediction with big data: a deep learning approach," IEEE Trans. Intell. Transp. Syst. 16(2), 865-873 (2015).
299. M. Elawady, "Sparse coral classification using deep convolutional neural networks," PhD Thesis, University of Burgundy, University of Girona, Heriot-Watt University (2014).
300. H. Qin et al., "DeepFish: accurate underwater live fish recognition with a deep architecture," Neurocomputing 187, 49-58 (2016).
301. H. Qin et al., "When underwater imagery analysis meets deep learning: a solution at the age of big visual data," in 7th Int. Symp. on Image and Signal Processing and Analysis (ISPA '15), pp. 259-264 (2015).
302. D. P. Williams, "Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks," in Proc. 23rd Int. Conf. on Pattern Recognition (ICPR) (2016).
303. F. Alidoost and H. Arefi, "Knowledge based 3D building model recognition using convolutional neural networks from Lidar and aerial imageries," in ISPRS-Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 833-840 (2016).
304. C.-A. Brust et al., "Efficient convolutional patch networks for scene understanding," in Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1-9 (2015).
305. S. De and A. Bhattacharya, "Urban classification using PolSAR data and deep learning," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), pp. 353-356, IEEE (2015).
306. Z. Huang et al., "Building extraction from multi-source remote sensing images via deep deconvolution neural networks," in Geoscience and Remote Sensing Symp. (IGARSS), pp. 1835-1838, IEEE (2016).
307. D. Marmanis et al., "Deep neural networks for above-ground detection in very high spatial resolution digital elevation models," in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4, pp. 103-110 (2015).
308. S. Saito and Y. Aoki, "Building and road detection from large aerial imagery," Proc. SPIE 9405, 94050K (2015).
309. M. Vakalopoulou et al., "Building detection in very high resolution multispectral data with deep learning features," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS 2015), pp. 1873-1876 (2015).
310. M. Xie et al., "Transfer learning from deep features for remote sensing and poverty mapping," 16, arXiv:1510.00098 (2015).
311. W. Yao, P. Poleswki, and P. Krzystek, "Classification of urban aerial data based on pixel labelling with deep convolutional neural networks and logistic regression," Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLI-B7, 405-410 (2016).
312. Q. Zhang et al., "CNN based suburban building detection using monocular high resolution Google Earth images," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS 2016), pp. 661-664, IEEE (2016).
313. Z. Zhang et al., "A CNN based functional zone classification method for aerial images," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS 2016), pp. 5449-5452, IEEE (2016).
314. L. Cao et al., "Robust vehicle detection by combining deep features with exemplar classification," Neurocomputing 215, 225-231 (2016).
315. X. Chen et al., "Vehicle detection in satellite images by hybrid deep convolutional neural networks," IEEE Geosci. Remote Sens. Lett. 11(10), 1797-1801 (2014).
316. K. Goyal and D. Kaur, "A novel vehicle classification model for urban traffic surveillance using the deep neural network model," Int. J. Educ. Manage. Eng. 6(1), 18-31 (2016).
317. A. Hu et al., "Deep Boltzmann machines based vehicle recognition," in 26th Chinese Control and Decision Conf. (2014 CCDC), pp. 3033-3038 (2014).
318. J. Huang and S. You, "Vehicle detection in urban point clouds with orthogonal-view convolutional neural network," in 2016 IEEE Int. Conf. on Image Processing (ICIP), pp. 2593-2597 (2016).
319. B. Huval et al., "An empirical evaluation of deep learning on highway driving," 1-7, arXiv:1504.01716 (2015).
320. Q. Jiang et al., "Deep neural networks-based vehicle detection in satellite images," in Int. Symp. on Bioelectronics and Bioinformatics (ISBB), pp. 184-187, IEEE (2015).
321. G. V. Konoplich, E. O. Putin, and A. A. Filchenkov, "Application of deep learning to the problem of vehicle detection in UAV images," in XIX IEEE Int. Conf. on Soft Computing and Measurements (SCM), pp. 4-6, IEEE (2016).
322. A. Krishnan and J. Larsson, "Vehicle detection and road scene segmentation using deep learning," PhD Thesis, Chalmers University of Technology (2016).
323. S. Lange, F. Ulbrich, and D. Goehring, "Online vehicle detection using deep neural networks and lidar based preselected image patches," in Proc. IEEE Intelligent Vehicles Symp., pp. 954-959 (2016).
324. B. Li, "3D fully convolutional network for vehicle detection in point cloud," arXiv:1611.08069 (2016).
325. H. Wang, Y. Cai, and L. Chen, "A vehicle detection algorithm based on deep belief network," Sci. World J. 2014, 647380 (2014).
326. J.-G. Wang et al., "Appearance-based brake-lights recognition using deep learning and vehicle detection," in Intelligent Vehicles Symp., pp. 815-820, IEEE (2016).
327. H. Wang et al., "Night-time vehicle sensing in far infrared image with deep learning," J. Sens. 2016, 3403451 (2015).
328. R. J. Firth, "A novel recurrent convolutional neural network for ocean and weather forecasting," PhD Thesis, Louisiana State University (2016).
329. R. Kovordányi and C. Roy, "Cyclone track forecasting based on satellite images using artificial neural networks," ISPRS J. Photogramm. Remote Sens. 64(6), 513-521 (2009).
330. C. Yang and J. Guo, "Improved cloud phase retrieval approaches for China's FY-3A/VIRR multi-channel data using artificial neural networks," Optik 127(4), 1797-1803 (2016).
331. Y. Chen, X. Zhao, and X. Jia, "Spectral-spatial classification of hyperspectral data based on deep belief network," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2381-2392 (2015).
332. J. M. P. Nascimento and J. M. B. Dias, "Vertex component analysis: a fast algorithm to unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens. 43(4), 898-910 (2005).
333. T. H. Chan et al., "PCANet: a simple deep learning baseline for image classification?," IEEE Trans. Image Process. 24(12), 5017-5032 (2015).
334. Y. Chen et al., "Deep feature extraction and classification of hyperspectral images based on convolutional neural networks," IEEE Trans. Geosci. Remote Sens. 54(10), 6232-6251 (2016).
335. J. Donahue et al., "DeCAF: a deep convolutional activation feature for generic visual recognition," ICML 32, 647-655 (2014).
336. A. Zelener and I. Stamos, "CNN-based object segmentation in urban lidar with missing points," in Fourth Int. Conf. on 3D Vision (3DV), pp. 417-425, IEEE (2016).
337. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3431-3440 (2015).
338. J. E. Ball, D. T. Anderson, and S. Samiappan, "Hyperspectral band selection based on the aggregation of proximity measures for automated target detection," Proc. SPIE 9088, 908814 (2014).
339. J. E. Ball and L. M. Bruce, "Level set hyperspectral image classification using best band analysis," IEEE Trans. Geosci. Remote Sens. 45(10), 3022-3027 (2007).
340. J. E. Ball et al., "Level set hyperspectral image segmentation using spectral information divergence-based best band selection," in IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS 2007), pp. 4053-4056, IEEE (2007).
341. D. T. Anderson and A. Zare, "Spectral unmixing cluster validity index for multiple sets of endmembers," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(4), 1282-1295 (2012).
342. M. E. Winter, "Comparison of approaches for determining end-members in hyperspectral data," in Aerospace Conf. Proc., pp. 305-313, IEEE (2000).
343. A. S. Charles, B. A. Olshausen, and C. J. Rozell, "Learning sparse codes for hyperspectral imagery," IEEE J. Sel. Top. Signal Process. 5(5), 963-978 (2011).
344. A. Romero, C. Gatta, and G. Camps-Valls, "Unsupervised deep feature extraction of hyperspectral images," in Proc. 6th Workshop Hyperspectral Image Signal Processing Evaluation Remote Sensing (WHISPERS) (2014).
345. J. E. Ball, L. M. Bruce, and N. H. Younan, "Hyperspectral pixel unmixing via spectral band selection and DC-insensitive singular value decomposition," IEEE Geosci. Remote Sens. Lett. 4(3), 382-386 (2007).
346. P. M. Atkinson and A. Tatnall, "Introduction neural networks in remote sensing," Int. J. Remote Sens. 18(4), 699-709 (1997).
347. G. Cavallaro et al., "On understanding big data impacts in remotely sensed image classification using support vector machine methods," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(10), 4634-4646 (2015).
348. M. Egmont-Petersen, D. DeRidder, and H. Handels, "Image processing with neural networks-a review," Pattern Recognit. 35, 2279-2301 (2002).
349. G. Huang et al., "Trends in extreme learning machines: a review," Neural Networks 61, 32-48 (2015).
350. X. Jia, B.-C. Kuo, and M. M. Crawford, "Feature mining for hyperspectral image classification," Proc. IEEE 101(3), 676-697 (2013).
351. D. Lu and Q. Weng, "A survey of image classification methods and techniques for improving classification performance," Int. J. Remote Sens. 28(5), 823-870 (2007).
352. H. Petersson, D. Gustafsson, and D. Bergstrom, "Hyperspectral image analysis using deep learning-a review," in 6th Int. Conf. on Image Processing Theory Tools and Applications (IPTA), pp. 1-6, IEEE (2016).
353. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ. 113, S110-S122 (2009).
354. W. Wang et al., "A review of road extraction from remote sensing images," J. Traffic Transp. Eng. 3(3), 271-282 (2016).
355. "IEEE GRSS data fusion contest," http://www.grss-ieee.org/community/technicalcommittees/data-fusion/ (20 November 2016).
356. "Indian Pines dataset," http://dynamo.ecn.purdue.edu/biehl/MultiSpec (20 November 2016).
357. "Kennedy Space Center," http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral-Remote-Sensing-Scenes#Kennedy-Space-Center-.28KSC.29 (20 November 2016).
358. "Pavia dataset," http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral-Remote-Sensing-Scenes#Pavia-Centre-and-University (20 November 2016).
359. "Salinas dataset," http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral-Remote-Sensing-Scenes#Salinas (20 November 2016).
360. "Washington DC Mall," https://engineering.purdue.edu/biehl/MultiSpec/hyperspectral.html (20 November 2016).
361. G. Cheng and J. Han, "A survey on object detection in optical remote sensing images," ISPRS J. Photogramm. Remote Sens. 117, 11-28 (2016).
362. Y. Chen et al., "Deep learning-based classification of hyperspectral data," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2094-2107 (2014).
363. V. Slavkovikj et al., "Hyperspectral image classification with convolutional neural networks," in Proc. of the 23rd ACM Int. Conf. on Multimedia, pp. 1159-1162, ACM (2015).
364. C. Tao et al., "Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification," IEEE Geosci. Remote Sens. Lett. 12(12), 2438-2442 (2015).
365. Y. LeCun, "Learning invariant feature hierarchies," in Computer Vision (ECCV 2012), pp. 496-505, Springer (2012).
366. M. Pal, "Kernel methods in remote sensing: a review," ISH J. Hydraul. Eng. 15(Suppl. 1), 194-215 (2009).
367. E. M. Abdel-Rahman and F. B. Ahmed, "The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature," Int. J. Remote Sens. 29(13), 3753-3767 (2008).
368. I. Ali et al., "Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data," Remote Sens. 7(12), 16398-16421 (2015).
369. E. Adam, O. Mutanga, and D. Rugege, "Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review," Wetlands Ecol. Manage. 18(3), 281-296 (2010).
370. S. L. Ozesmi and M. E. Bauer, "Satellite remote sensing of wetlands," Wetlands Ecol. Manage. 10(5), 381-402 (2002).
371. W. A. Dorigo et al., "A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling," Int. J. Appl. Earth Obs. Geoinf. 9(2), 165-193 (2007).
372. C. Kuenzer et al., "Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks," Int. J. Remote Sens. 35(18), 6599-6647 (2014).
373. K. Wang et al., "Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists," Sensors 10(11), 9647-9667 (2010).
374. F. E. Fassnacht et al., "Review of studies on tree species classification from remotely sensed data," Remote Sens. Environ. 186, 64-87 (2016).
375. A. Mahendran and A. Vedaldi, "Understanding deep image representations by inverting them," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 5188-5196 (2015).
376. J. Yosinski et al., "Understanding neural networks through deep visualization," arXiv:1506.06579 (2015).
377. K. Simonyan, A. Vedaldi, and A. Zisserman, "Deep inside convolutional networks: visualising image classification models and saliency maps," arXiv:1312.6034 (2013).
378. D. Erhan et al., "Visualizing higher-layer features of a deep network," Univ. Montreal 1341, 3 (2009).
379. J. A. Benediktsson, J. Chanussot, and W.W. Moon, "Very high-resolution remote sensing: challenges and opportunities," Proc. IEEE 100(6), 1907-1910 (2012).
380. J. Fohringer et al., "Social media as an information source for rapid flood inundation mapping," Nat. Hazards Earth Syst. Sci. 15(12), 2725-2738 (2015).
381. V. Frias-Martinez and E. Frias-Martinez, "Spectral clustering for sensing urban land use using Twitter activity," Eng. Appl. Artif. Intell. 35, 237-245 (2014).
382. T. Kohonen, "The self-organizing map," Neurocomputing 21(1), 1-6 (1998).
383. S. E. Middleton, L. Middleton, and S. Modafferi, "Real-time crisis mapping of natural disasters using social media," IEEE Intell. Syst. 29(2), 9-17 (2014).
384. V. K. Singh, "Social pixels: genesis and evaluation," in Proc. of the 18th ACM Int. Conf. on Multimedia (ACMM), pp. 481-490 (2010).
385. D. Sui and M. Goodchild, "The convergence of GIS and social media: challenges for GIScience," Int. J. Geogr. Inf. Sci. 25(11), 1737-1748 (2011).
386. A. J. Pinar et al., "Efficient multiple kernel classification using feature and decision level fusion," IEEE Trans. Fuzzy Syst. PP(99), 1-1 (2016).
387. D. T. Anderson et al., "Extension of the fuzzy integral for general fuzzy set-valued information," IEEE Trans. Fuzzy Syst. 22, 1625-1639 (2014).
388. P. Ghamisi, B. Hfle, and X. X. Zhu, "Hyperspectral and lidar data fusion using extinction profiles and deep convolutional neural network," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. PP(99), 1-14 (2016).
389. J. Ngiam et al., "Multimodal deep learning," in Int. Conf. on Machine Learning (ICML), pp. 689-696, Omnipress (2011).
390. Z. Kira et al., "Long-range pedestrian detection using stereo and a cascade of convolutional network classifiers," in IEEE Int. Conf. on Intelligent Robots and Systems, pp. 2396-2403 (2012).
391. F. Rottensteiner et al., "The ISPRS benchmark on urban object classification and 3D building reconstruction," ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. I-3, 293-298 (2012).
392. J. Ngiam et al., "Sparse filtering," in Advances in Neural Information Processing Systems, J. Shawe-Taylor et al., Eds., pp. 1125-1133, Curran Associates, Inc. (2011).
393. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," in European Conf. on Computational Learning Theory, pp. 23-37, Springer (1995).
394. Q. Zhao et al., "Three-class change detection in synthetic aperture radar images based on deep belief network," in Bio-Inspired Computing-Theories and Applications, pp. 696-705, Springer (2015).
395. A. P. Tewkesbury et al., "A critical synthesis of remotely sensed optical image change detection techniques," Remote Sens. Environ. 160, 1-14 (2015).
396. C. Brekke and A. H. S. Solberg, "Oil spill detection by satellite remote sensing," Remote Sens. Environ. 95(1), 1-13 (2005).
397. H. Mayer, "Automatic object extraction from aerial imagery a survey focusing on buildings," Comput. Vision Image Understanding 74(2), 138-149 (1999).
398. B. Somers et al., "Endmember variability in spectral mixture analysis: a review," Remote Sens. Environ. 115(7), 1603-1616 (2011).
399. D. Tuia, C. Persello, and L. Bruzzone, "Domain adaptation for the classification of remote sensing data: an overview of recent advances," IEEE Geosci. Remote Sens. Mag. 4(2), 41-57 (2016).
400. S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng. 22(10), 1345-1359 (2010).
401. Y. Yang and S. Newsam, "Bag-of-visual-words and spatial extensions for land-use classification," in Proc. of the 18th SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, pp. 270-279, ACM (2010).
402. V. Risojevic, S. Momic, and Z. Babic, "Gabor descriptors for aerial image classification," in Int. Conf. on Adaptive and Natural Computing Algorithms, pp. 51-60, Springer (2011).
403. K. Chatfield et al., "Return of the devil in the details: delving deep into convolutional nets," arXiv:1405.3531 (2014).
404. G. Sheng et al., "High-resolution satellite scene classification using a sparse coding based multiple feature combination," Int. J. Remote Sens. 33(8), 2395-2412 (2012).
405. S. H. Lee et al., "How deep learning extracts and learns leaf features for plant classification," Pattern Recognit. 71, 1-13 (2017).
406. A. Joly et al., "LifeCLEF 2016: multimedia life species identification challenges," in Int. Conf. of the Cross-Language Evaluation Forum for European Languages, pp. 286-310, Springer (2016).
407. S. H. Lee et al., "Deep-plant: plant identification with convolutional neural networks," in IEEE Int. Conf. on Image Processing (ICIP 2015), pp. 452-456 (2015).
408. Z. Ding, N. Nasrabadi, and Y. Fu, "Deep transfer learning for automatic target classification: MWIR to LWIR," Proc. SPIE 9844, 984408 (2016).
409. X. Glorot and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in Proc. of the Thirteenth Int. Conf. on Artificial Intelligence and Statistics, pp. 249-256 (2010).
410. J. Sokolic et al., "Robust large margin deep neural networks," arXiv:1605.08254 (2016).
411. D. Erhan, A. Courville, and P. Vincent, "Why does unsupervised pre-training help deep learning?," J. Mach. Learn. Res. 11, 625-660 (2010).
412. K. He et al., "Delving deep into rectifiers: surpassing human-level performance on ImageNet classification," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 1026-1034 (2015).
413. N. Srivastava et al., "Dropout: a simple way to prevent neural networks from overfitting," J. Mach. Learn. Res. 15(1), 1929-1958 (2014).
414. S. Ioffe and C. Szegedy, "Batch normalization: accelerating deep network training by reducing internal covariate shift," arXiv:1502.03167 (2015).
415. Q. V. Le et al., "On optimization methods for deep learning," in Proc. of the 28th Int. Conf. on Machine Learning (ICML), pp. 265-272 (2011).
416. S. Ruder, "An overview of gradient descent optimization algorithms," arXiv:1609.04747 (2016).
417. M. D. Zeiler, "Adadelta: an adaptive learning rate method," arXiv:1212.5701 (2012).
418. T. Schaul, S. Zhang, and Y. LeCun, "No more pesky learning rates," in Int. Conf. on Machine Learning (ICML), pp. 343-351 (2013).
419. D. Balduzzi, B. McWilliams, and T. Butler-Yeoman, "Neural Taylor approximations: convergence and exploration in rectifier networks," arXiv:1611.02345 (2016).