Deep convolutional neural networks for the classification of snapshot mosaic hyperspectral imagery

IS and T International Symposium on Electronic Imaging Science and Technology

Volumn , Issue , 2017, Pages 185-190

  Fotiadou, Konstantina ^a,b   Tsagkatakis, Grigorios ^a   Tsakalides, Panagiotis ^a,b  

^a INSTITUTE OF COMPUTER SCIENCE   (Greece)

^b UNIVERSITY OF CRETE   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; DEEP NEURAL NETWORKS; IMAGE CLASSIFICATION; LEARNING SYSTEMS; NEURAL NETWORKS; SPECTROSCOPY;

CLASSIFICATION RESULTS; CONVOLUTIONAL NEURAL NETWORK; CURSE OF DIMENSIONALITY; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL SENSORS; MACHINE LEARNING TECHNIQUES; MULTI-CLASS CLASSIFIER; STATE-OF-THE-ART SCHEME;

CLASSIFICATION (OF INFORMATION);

EID: 85041506038     PISSN: None     EISSN: 24701173     Source Type: Conference Proceeding    
DOI: 10.2352/ISSN.2470-1173.2017.17.COIMG-445     Document Type: Conference Paper

References (35)

1. J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. M. Nasrabadi, and J. Chanussot, Hyperspectral remote sensing data analysis and future challenges, Geoscience and Remote Sensing Magazine, IEEE, 1(2), pg. 6-36. (2013).
2. N. Hagen, and M. W. Kudenov, Review of snapshot spectral imaging technologies, Optical Engineering 52(9), 090901-090901, (2013).
3. F. Melgani and L. Bruzzone, Classification of hyperspectral remotesensing images with support vector machines, IEEE Trans. Geosci. Remote Sensing, Vol. 42, no. 8, pp. 1778-1790, (2004).
4. G. Tsagkatakis, and P. Tsakalides, Compressed hyperspectral sensing, IS & T/SPIE Electronic Imaging, International Society for Optics and Photonics. (2015).
5. D. G. Lowe (1999). Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on (Vol. 2, pp. 1150-1157).
6. N. Dalal, and B. Triggs (2005, June). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (Vol. 1, pp. 886-893). IEEE.
7. C. Lee, and D.A. Landgrebe (1993). Feature extraction based on decision boundaries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4), 388-400.
8. S. Kumar, J. Ghosh, and M.M. Crawford (2001). Best-bases feature extraction algorithms for classification of hyperspectral data. IEEE Transactions on Geoscience and remote sensing, 39(7), 1368-1379.
9. P. Y. Simard, D. Steinkraus, and J. C. Platt. Best practices for convolutional neural networks applied to visual document analysis. In ICDAR (Vol. 3, pp. 958-962).
10. Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828.
11. J.A. Suykens, and J. Vandewalle (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.
12. Y. Tang (2013). Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239.
13. B. Geelen, T Nicolaas, L. Andy, A compact snapshot multispectral imager with a monolithically integrated per-pixel filter mosaic, Spie Moems-Mems. Intern. Society for Optics and Photonics. (2014).
14. B. Geelen, et al, A tiny VIS-NIR snapshot multispectral camera, SPIE OPTO. International Society for Optics and Photonics. (2015).
15. A. Lambrechts, et al, A CMOS-compatible, integrated approach to hyper-and multispectral imaging, IEEE International Electron Devices Meeting (IEDM), (2014).
16. Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature 521.7553 (2015): 436-444.
17. G. Tsagkatakis, and P. Tsakalides. Deep Feature Learning for Hyperspectral Image Classification and Land Cover Estimation. ESA Symbosium, 2016.
18. K. Karalas, et al: Deep learning for multi-label land cover classification, SPIE Remote Sensing. Intern. Society for Optics and Photonics. (2015).
19. S. Lawrence, C. L. Giles, A.C. Tsoi, and A.D. Back (1997). Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), 98-113.
20. J. Long, E. Shelhamer, and T. Darrell. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3431-3440).
21. M. Vakalopoulou, K. Karantzalos, N. Komodakis, and N. Paragios. Building detection in very high resolution multispectral data with deep learning features. In Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, pages 1873-1876. IEEE, 2015.
22. Y. Zhong, F. Fei, and L. Zhang. Large patch convolutional neural networks for the scene classification of high spatial resolution imagery. Journal of Applied Remote Sensing, 10(2):025006-025006, 2016.
23. E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez (2017). Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification. In IEEE Transactions on Geoscience and Remote Sensing, 55(2), 645-657.
24. Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi(2016). Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232-6251.
25. J. Yue, W. Zhao, S. Mao, and H. Liu. Spectralspatial classification of hyperspectral images using deep convolutional neural networks. In Remote Sensing Letters, Vol. 6, no. 6, pp. 468-477, 2015.
26. D. Bremner, E. Demaine, J. Erickson, J. Iacono, S. Langerman, P. Morin, G. Toussaint, Output-sensitive algorithms for computing nearestneighbor decision boundaries, Discrete and Computational Geometry, 2005, pp. 593-604.
27. T. Cover, P. Hart. Nearest-neighbor pattern classification, Information Theory, IEEE Transactions on, Jan. 1967, pp. 21-27.
28. J. I. N. H. O. KIM, B. S. Kim, and S. Savarese (2012). Comparing image classification methods: K-nearest-neighbor and support-vector-machines. Ann Arbor, 1001, 48109-2122.
29. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Chapman & Hall, Boca Raton, 1993.
30. P. N. Tan, M. Steinbach, and & V. Kumar (2006). Classification: basic concepts, decision trees, and model evaluation. Introduction to data mining, 1, 145-205.
31. K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis. Deep supervised learning for hyperspectral data classification through convolutional neural networks. In IEEE IGARSS. IEEE, 2015, pp. 4959-4962.
32. N. Srivastava, G.E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929-1958.
33. A. Ng. "Sparse autoencoder." CS294A Lecture notes 72 (2011): 1-19.
34. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems.
35. Y. LeCun, L. Bottou, G.B. Orr, K. R. Muller: Efficient backprop. In Neural networks: Tricks of the trade (pp. 9-48). Springer Berlin Heidelberg. (2012)