Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

IEEE Transactions on Geoscience and Remote Sensing

Volumn 54, Issue 10, 2016, Pages 6232-6251

  Chen, Yushi ^a   Jiang, Hanlu ^a   Li, Chunyang ^a   Jia, Xiuping ^b   Ghamisi, Pedram ^c,d  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^d GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Convolutional neural network (CNN); deep learning; feature extraction (FE); Hyperspectral image (HSI) classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; EXTRACTION; FEATURE EXTRACTION; IMAGE PROCESSING; INDEPENDENT COMPONENT ANALYSIS; NEURAL NETWORKS; SPACE PLATFORMS; SPECTROSCOPY; TARGET TRACKING;

CONVOLUTIONAL NEURAL NETWORK; FEATURE EXTRACTION AND CLASSIFICATION; HIGH DIMENSIONALITY; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGES; KENNEDY SPACE CENTERS; STATE-OF-THE-ART METHODS;

IMAGE CLASSIFICATION;

ARTIFICIAL NEURAL NETWORK; DATA PROCESSING; DATA SET; DETECTION METHOD; IMAGE CLASSIFICATION; LEARNING; MULTISPECTRAL IMAGE; PERFORMANCE ASSESSMENT;

FLORIDA [UNITED STATES]; ITALY; KENNEDY SPACE CENTER; LOMBARDY; PAVIA; UNITED STATES;

EID: 84978805819     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2016.2584107     Document Type: Article

References (51)

1. J. A. Benediktsson and P. Ghamisi, Spectral-Spatial Classification of Hyperspectral Remote Sensing Images. Boston, MA, USA: Artech House, 2015.
2. G. Hughes, "On the mean accuracy of statistical pattern recognizers, " IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.
3. J. B. Dias et al., "Hyperspectral remote sensing data analysis and future challenges, " IEEE Geosci. Remote Sens. Mag., vol. 1, no. 2, pp. 6-36, Feb. 2013.
4. X. Jia, B. Kuo, M. M. Crawford, "Feature mining for hyperspectral image classification, " Proc. IEEE, vol. 101, no. 3, pp. 676-679, Mar. 2013.
5. G. Licciardi, P. R. Marpu, J. Chanussot, J. A. Benediktsson, "Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles, " IEEE Geosci. Remote Sens. Lett., vol. 9, no. 3, pp. 447-451, May 2011.
6. A. Villa, J. A. Benediktsson, J. Chanussot, C. Jutten, "Hyperspectral image classification with independent component discriminant analysis, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 12, pp. 4865-4876, Dec. 2011.
7. T. V. Bandos, L. Bruzzone, G. Camps-Valls, "Classification of hyperspectral images with regularized linear discriminant analysis, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 862-873, Mar. 2009.
8. L. M. Bruce, C. H. Koger, J. Li, "Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction, " IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp. 2331-2338, Oct. 2002.
9. L. O. Jimenez and D. A. Landgrebe, "Hyperspectral data analysis and supervised feature reduction via projection pursuit, " IEEE Trans. Geosci. Remote Sens., vol. 37, no. 6, pp. 2653-2667, Nov. 1999.
10. D. Lunga, S. Prasad, M. M. Crawford, O. Ersoy, "Manifold-learningbased feature extraction for classification of hyperspectral data: A review of advances in manifold learning, " IEEE Signal Process. Mag., vol. 31, no. 1, pp. 55-66, Jan. 2014.
11. T. Han, D. Goodenough, "Investigation of nonlinearity in hyperspectral imagery using surrogate data methods, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 10, pp. 2840-2847, Oct. 2008.
12. B. Tenenbaum, V. Silva, C. Langford, "A global geometric framework for nonlinear dimensionality reduction, " Science, vol. 290, no. 5500, pp. 2319-2323, Dec. 2000.
13. S. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding, " Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000.
14. C. M. Bachmann, T. L. Ainsworth, R. A. Fusina, "Improved manifold coordinate representations of large-scale hyperspectral scenes, " IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2786-2803, Oct. 2006.
15. B. Scholkopf and A. J. Smola, Learning With Kernels. Cambridge, MA, USA: MIT Press, 2002.
16. B. C. Kuo, C. H. Li, J. M. Yang, "Kernel nonparametric weighted feature extraction for hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 4, pp. 1139-1155, Apr. 2009.
17. A. Plaza, J. Plaza, G. Martin, "Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data, " in Proc. IEEE Int. Workshop Mach. Learn. Signal Process., Grenoble, France, 2009, pp. 1-6.
18. M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, J. C. Tilton, "Advances in spectral-spatial classification of hyperspectral images, " Proc. IEEE, vol. 101, no. 3, pp. 652-675, Mar. 2013.
19. Y. Tarabalka, M. Fauvel, J. Chanussot, J. A. Benediktsson, "SVM-and MRF-based method for accurate classification of hyperspectral images, " IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 736-740, Oct. 2010.
20. M. Fauvel, J. A. Benediktsson, J. Chanussot, J. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 11, pp. 3804-3814, Nov. 2008.
21. J. Li, J. M. Bioucas-Dias, A. Plaza, "Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 844-856, Feb. 2013.
22. Y. Chen, N. M. Nasrabadi, T. D. Tran, "Hyperspectral image classification using dictionary-based sparse representation, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3973-3985, Oct. 2011.
23. B. Song, J. Li, J. M. Bioucas-Dias, J. A. Benediktsson, "Remotely sensed image classification using sparse representations of morphological attribute profiles, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 5122-5136, Aug. 2013.
24. Y. Bengio, A. Courville, P. Vincent, "Representation learning. A review and new perspectives, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
25. N. Kruger et al., "Deep hierarchies in primate visual cortex what can we learn for computer vision?" IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1847-1871, Aug. 2013.
26. A. Krizhevsky, I. Sutskever, G. Hinton, "ImageNet classification with deep convolutional neural networks, " in Proc. Neural Inf. Process. Syst., Lake Tahoe, NV, USA, 2012, pp. 1106-1114.
27. G. Hinton and R. Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006.
28. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, "Gradient-based learning applied to document recognition, " Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
29. Y. Chen, Z. Lin, X. Zhao, G. Wang, "Deep learning-based classification of hyperspectral data, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2094-2107, Jun. 2014.
30. Y. Chen, X. Zhao, X. Jia, "Spectral-spatial classification of hyperspectral data based on deep belief network, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 1-12, Jun. 2015.
31. A. Romero, C. Gatta, G. Camps-Valls, "Unsupervised deep feature extraction for remote sensing image classification, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 3, pp. 1349-1362, Mar. 2016.
32. Y. LeCun, C. Cortes, C. Burges, TheMNIST Database of Handwritten Digits. [Online]. Available: http://yann. lecun. com/exdb/mnist/
33. J. Deng and F. Li, "ImageNet: A large-scale hierarchical image database, " in Proc. CVPR, Miami, FL, USA, 2009, pp. 248-255.
34. G. E. Hinton, S. Osindero, Y. Teh, "A fast learning algorithm for deep belief nets, " Neural Comput., vol. 18, no. 7, pp. 1527-1554, Jul. 2006.
35. N. LeRoux and Y. Bengio, "Deep belief networks are compact universal approximators, " Neural Comput., vol. 22, no. 8, pp. 2192-2207, Aug. 2010.
36. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P. Manzagol, "Stacked denoising autoencoders, " J. Mach. Learn. Res., vol. 11, no. 12, pp. 3371-3408, Dec. 2010.
37. Z. Zuo et al., "Learning contextual dependence with convolutional hierarchical recurrent neural networks, " IEEE Trans. Image Process., vol. 25, no. 7, pp. 2983-2996, Jul. 2016.
38. C. Szegedy et al., "Going deeper with convolutions, " in Proc. IEEE CVPR, Boston, MA, USA, 2015, pp. 1-9.
39. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition, " in Proc. ICLR, 2015, pp. 1-14.
40. R. Girshick, J. Donahue, T. Darrell, J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation, " in Proc. IEEE CVPR, Columbus, OH, USA, 2014, pp. 581-587.
41. A. E. Hoerl and R. W. Kennard, "Ridge regression: Biased estimation for non-orthogonal problems, " Technimetrics, vol. 12, no. 1, pp. 55-67, Jan. 1970.
42. V. Nair and G. E. Hinton, "Rectified linear units improve restricted Boltzmann machines, " in Proc. Int. Conf. Mach. Learn., Haifa, Israel, 2010, pp. 807-814.
43. G. E. Hinton et al., "Improving neural networks by preventing coadaptation of feature detectors, " Comput. Sci., vol. 3, no. 4, pp. 212-223, 2012.
44. R. E. Edwards, H. Zhang, L. E. Parker, "Approximate l-fold crossvalidation with least squares SVM and kernel ridge regression, " in Proc. 12th ICMLA, Miami, FL, USA, Dec. 2013, pp. 58-64.
45. W. Hofmann, "Remote sensing: The quantitative approach, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 3, no. 6, pp. 713-714, Jun. 1981.
46. D. J. Bartholomew, F. Steele, J. Galbraith, I. Moustaki, "Analysis of multivariate social science data, " Struct. Equation Model. Multidisciplinary J., vol. 18, no. 4, pp. 686-693, Apr. 2011.
47. H. Yang, F. Qin, Y. Wang, "LLE-PLS nonlinear modeling method for near infrared spectroscopy and its application, " Spectrosc. Spectral Anal., vol. 27, no. 10, pp. 1955-1958, Oct. 2007.
48. C. Chang and C. Lin, "LIBSVM: A library for support vector machines, " ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1-27, Mar. 2011.
49. L. G. Chova, D. Tuia, G. Moser, G. C. Valls, "Multimodal classification of remote sensing images: A review and future directions, " Proc. IEEE, vol. 103, no. 9, pp. 1560-1584, Nov. 2015.
50. C. Tao, H. Pan, Y. Li, Z. Zou, "Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 12, pp. 2438-2442, Dec. 2015.
51. J. A. Benediktsson, J. A. Palmason, J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles, " IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 480-491, Mar. 2005.