Hyperspectral imagery classification using sparse representations of convolutional neural network features

Remote Sensing

Volumn 8, Issue 2, 2016, Pages

  Liang, Heming ^a   Li, Qi ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

Deep features; Deep learning; Remote sensing image classific; Sparse representation

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; DATA MINING; IMAGE RECONSTRUCTION; NEURAL NETWORKS; REMOTE SENSING; SPECTROSCOPY;

CLASSIFICATION FRAMEWORK; CONVOLUTIONAL NEURAL NETWORK; DEEP FEATURES; DEEP LEARNING; HYPERSPECTRAL IMAGERY CLASSIFICATIONS; LOW DIMENSIONAL STRUCTURE; REMOTE SENSING IMAGES; SPARSE REPRESENTATION;

IMAGE CLASSIFICATION;

EID: 84962611241     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8020099     Document Type: Article

References (35)

1. Bruckstein, A.M.; Donoho, D.L.; Elad, M. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 2009, 51, 34-81.
2. Zhang, H.; Li, J.; Huang, Y.; Zhang, L. A nonlocal weighted joint sparse representation classification method for hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 2056-2065.
3. Chen, Y.; Nasrabadi, N.; Tran, T. Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3973-3985.
4. Tang, Y.Y.; Yuan, H.; Li, L. Manifold-based sparse representation for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2014, 52, 7606-7618.
5. Fauvel, M.; Benediktsson, J.; Chanussot, J.; Sveinsson, J. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans. Geosci. Remote Sens. 2008, 46, 3804-3814.
6. Bernabe, S.; Reddy Marpu, P.; Plaza, A.; Dalla Mura, M.; Atli Benediktsson, J. Spatial classification of multispectral images using kernel feature space representation. IEEE Geosci. Remote Sens. Lett. 2014, 11, 288-292.
7. Benediktsson, J.; Pesaresi, M.; Amason, K. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1940-1949.
8. Plaza, A.; Martinez, P.; Plaza, J.; Perez, R. Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Trans. Geosci. Remote Sens. 2005, 43, 466-479.
9. Benediktsson, J.; Palmason, J.; Sveinsson, J. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote Sens. 2005, 43, 480-491.
10. Dalla Mura, M.; Benediktsson, J.; Waske, B.; Bruzzone, L. Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans. Geosci. Remote Sens. 2010, 48, 3747-3762.
11. Song, B.; Li, J.; Dalla Mura, M.; Li, P.; Plaza, A.; Bioucas-Dias, J.; Atli Benediktsson, J.; Chanussot, J. Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE Trans. Geosci. Remote Sens. 2014, 52, 5122-5136.
12. Li, J.; Zhang, H.; Zhang, L. Supervised segmentation of very high resolution images by the use of extended morphological attribute profiles and a sparse transform. IEEE Geosci. Remote Sens. Lett. 2014, 11, 1409-1413.
13. Fang, L.; Li, S.; Kang, X.; Benediktsson, J. Spatial hyperspectral image classification via multiscale adaptive sparse representation. IEEE Trans. Geosci. Remote Sens. 2014, 52, 7738-7749.
14. Zhao, W.; Guo, Z.; Yue, J.; Zhang, X.; Luo, L. On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery. Int. J. Remote Sens. 2015, 36, 3368-3379.
15. Ji, R.; Gao, Y.; Hong, R.; Liu, Q.; Tao, D.; Li, X. Spectral-spatial constraint hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2014, 52, 1811-1824.
16. Hinton, G.E.; Osindero, S.; Teh, Y.W. A fast learning algorithm for deep belief nets. Neural Comput. 2006, 18, 1527-1554.
17. Le, Q.V. Building high-level features using large scale unsupervised learning. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada, 26-31 May 2013; pp. 8595-8598.
18. Rifai, S.; Vincent, P.; Muller, X.; Glorot, X.; Bengio, Y. Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), Bellevue,WA, USA, 28 June-2 July 2011; pp. 833-840.
19. Razavian, A.S.; Azizpour, H.; Sullivan, J.; Carlsson, S. CNN features off-the-shelf: An astounding baseline for recognition. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Columbus, OH, USA, 23-28 June 2014; pp. 512-519.
20. Oquab, M.; Bottou, L.; Laptev, I.; Sivic, J. Learning and transferring mid-level image representations using convolutional neural networks. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 23-28 June 2014; pp. 1717-1724.
21. Vincent, P.; Larochelle, H.; Lajoie, I.; Bengio, Y.; Manzagol, P.A. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 2010, 11, 3371-3408.
22. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 3-8 December 2012; pp. 1097-1105.
23. Chen, Y.; Lin, Z.; Zhao, X.; Wang, G.; Gu, Y. Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 2094-2107.
24. Chen, X.; Xiang, S.; Liu, C.L.; Pan, C.H. Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 2014, 11, 1797-1801.
25. Yue, J.; Zhao, W.; Mao, S.; Liu, H. Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sens. Lett. 2015, 6, 468-477.
26. LeCun, Y.; Boser, B.; Denker, J.S.; Henderson, D.; Howard, R.E.; Hubbard, W.; Jackel, L.D. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989, 1, 541-551.
27. Arel, I.; Rose, D.; Karnowski, T. Deep machine learning-A new frontier in artificial intelligence research [research frontier]. IEEE Comput. Int. Mag. 2010, 5, 13-18.
28. Nebauer, C. Evaluation of convolutional neural networks for visual recognition. IEEE Trans. Neural Netw. 1998, 9, 685-696.
29. Wright, J.; Yang, A.; Ganesh, A.; Sastry, S.; Ma, Y. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31, 210-227.
30. Tropp, J. Greed is good: Algorithmic results for sparse approximation. IEEE Trans. Inf. Theory 2004, 50, 2231-2242.
31. Elad, M.; Aharon, M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 2006, 15, 3736-3745.
32. Egmont-Petersen, M.; de Ridder, D.; Handels, H. Image processing with neural networks-A review. Pattern Recognit. 2002, 35, 2279-2301.
33. Salakhutdinov, R.; Hinton, G.E. Deep boltzmann machines. In Proceedings of the International Conference on Artificial Intelligence and Statistics, Clearwater Beach, FL, USA, 16-18 April 2009; pp. 448-455.
34. Pedergnana, M.; Marpu, P.; Mura, M.; Benediktsson, J.; Bruzzone, L. A novel technique for optimal feature selection in attribute profiles based on genetic algorithms. IEEE Trans. Geosci. Remote Sens. 2013, 51, 3514-3528.
35. Marpu, P.; Pedergnana, M.; Mura, M.; Peeters, S.; Benediktsson, J.; Bruzzone, L. Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques. Int. J. Image Data Fusion 2012, 3, 269-298.