Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 55, Issue 10, 2017, Pages 5585-5599

  Jiao, Licheng ^a   Liang, Miaomiao ^a   Chen, Huan ^a   Yang, Shuyuan ^a   Liu, Hongying ^a   Cao, Xianghai ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Deep multiscale feature; feature fusion; fully convolutional network (FCN); hyperspectral image classification (HSIC); spatial distribution prediction

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; HYPERSPECTRAL IMAGING; IMAGE CLASSIFICATION; SPECTROSCOPY;

CONVOLUTIONAL NETWORKS; FEATURE FUSION; MORPHOLOGICAL CHARACTERISTIC; MULTI-SCALE FEATURES; PIXELWISE CLASSIFICATION; SPATIAL STRUCTURAL INFORMATION; SPECTRAL FEATURE EXTRACTION; STATE-OF-THE-ART PERFORMANCE;

SPATIAL DISTRIBUTION;

ACCURACY ASSESSMENT; ALGORITHM; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; NETWORK ANALYSIS; PIXEL; PREDICTION; SPATIAL DISTRIBUTION;

EID: 85021951898     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2017.2710079     Document Type: Article

References (42)

1. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113, no. 1, pp. 110-122, Sep. 2009.
2. S. Deng, Y. Xu, Y. He, J. Yin, and Z. Wu, "A hyperspectral image classification framework and its application," Inf. Sci., vol. 299, pp. 379-393, Apr. 2015.
3. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
4. G. Mercier and M. Lennon, "Support vector machines for hyperspectral image classification with spectral-based kernels," in Proc. IGARSS, Jul. 2003, pp. 288-290.
5. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2010.
6. F. Wang and C. Zhang, "Label propagation through linear neighborhoods," IEEE Trans. Knowl. Data Eng., vol. 20, no. 1, pp. 55-67, Jan. 2008.
7. X. Kong, M. K. Ng, and Z.-H. Zhou, "Transductive multilabel learning via label set propagation," IEEE Trans. Geosci. Remote Sens., vol. 25, no. 3, pp. 704-719, Mar. 2013.
8. X. Zhu, Z. Ghahramani, and J. D. Lafferty, "Semi-supervised learning using Gaussian fields and harmonic functions," in Proc. ICML, 2003, pp. 912-919.
9. Y. Chen, N. M. Nasrabadi, and 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.
10. H. Zhang, J. Li, Y. Huang, and L. Zhang, "A nonlocal weighted joint sparse representation classification method for hyperspectral imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2056-2065, Jun. 2014.
11. X. Sun, Q. Qu, N. M. Nasrabadi, and T. D. Tran, "Structured priors for sparse-representation-based hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 7, pp. 1235-1239, Jul. 2014.
12. L. Fang, S. Li, X. Kang, and J. A. Benediktsson, "Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7738-7749, Dec. 2014.
13. Y. Chen, N. M. Nasrabadi, and D. T. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 1233-1236, Jan. 2013.
14. J. Peng, Y. Zhou, and C. L. P. Chen, "Region-kernel-based support vector machines for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 4810-4824, Sep. 2015.
15. G. Camps-Valls, T. V. B. Marsheva, and D. Zhou, "Semi-supervised graph-based hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3044-3054, Oct. 2007.
16. H. Cheng, Z. Liu, and J. Yang, "Sparsity induced similarity measure for label propagation," in Proc. ICCV, 2009, pp. 317-324.
17. R. G. F. Soares, H. Chen, and X. Yao, "Semisupervised classification with cluster regularization," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 11, pp. 1779-1792, Nov. 2012.
18. W. Li and Q. Du, "Gabor-filtering-based nearest regularized subspace for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1012-1022, Apr. 2014.
19. S. Jia, J. Hu, Y. Xie, L. Shen, X. Jia, and Q. Li, "Gabor cube selection based multitask joint sparse representation for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 6, pp. 3174-3187, Jun. 2016.
20. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using watershed transformation," Pattern Recognit., vol. 43, no. 7, pp. 2367-2379, 2010.
21. L. He, Y. Li, X. Li, and W. Wu, "Spectral-spatial classification of hyperspectral images via spatial translation-invariant wavelet-based sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2696-2712, May 2015.
22. O. Regniers, L. Bombrun, V. Lafon, and C. Germain, "Supervised classification of very high resolution optical images using wavelet-based textural features," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 6, pp. 3722-3735, Jun. 2016.
23. Y. Gu, T. Liu, X. Jia, J. A. Benediktsson, and J. Chanussot, "Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 6, pp. 3235-3247, Jun. 2016.
24. Z. Xue, J. Li, L. Cheng, and P. Du, "Spectral-spatial classification of hyperspectral data via morphological component analysis-based image separation," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 70-84, Jan. 2015.
25. X. Kang, S. Li, and J. A. Benediktsson, "Feature extraction of hyperspectral images with image fusion and recursive filtering," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 6, pp. 3742-3752, Jun. 2014.
26. X. Kang, S. Li, L. Fang, and J. A. Benediktsson, "Intrinsic image decomposition for feature extraction of hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 4, pp. 2241-2253, Apr. 2015.
27. L. Fang, S. Li, W. Duan, J. Ren, and J. A. Benediktsson, "Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 12, pp. 6663-6674, Dec. 2015.
28. L. Fang, S. Li, X. Kang, and J. A. Benediktsson, "Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4186-4201, Aug. 2015.
29. Y. Chen, Z. Lin, X. Zhao, G. Wang, and Y. Gu, "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, and 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. 2381-2392, Jun. 2015.
31. Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, "Deep feature extraction and classification of hyperspectral images based on convolutional neural networks," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 6232-6251, Oct. 2016.
32. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proc. CVPR, 2015, pp. 3431-3440.
33. F. Hu, G. Xia, J. Hu, and L. Zhang, "Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing Imagery," Remote Sens., vol. 7, no. 11, pp. 14680-14707, Nov. 2015.
34. M. Oquab, L. Bottou, I. Laptev, and J. Sivic, "Learning and transferring mid-level image representations using convolutional neural networks," in Proc. CVPR, Jun. 2014, pp. 1717-1724.
35. M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in Proc. ECCV, 2013, pp. 818-833.
36. K. Simonyan and A. Zisserman. (Sep. 2014). "Very deep convolutional networks for large-scale image recognition." [Online]. Available: https://arxiv.org/abs/1409.1556
37. R. C. Gonzales and R. E. Woods, Digital Image Processing, 2nd ed. Englewood Cliffs, NJ, USA: Prentice-Hall, 2002.
38. L. van der Maaten and G. Hinton, "Visualizing data using t-SNE," J. Mach. Learn. Res., vol. 9, pp. 2579-2605, Nov. 2008.
39. C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1-27, 2011.
40. R. Weber, H. J. Schek, and S. Blott, "A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces," in Proc. VLDB, vol. 98. 1998, pp. 194-205.
41. M. Elad, "Sparse redundant representations," in From Theory to Applications in Signal and Image Processing, New York, NY, USA: Springer-Verlag, 2010.
42. J. L. Bentley, "Multidimensional binary search trees used for associative searching," Commun. ACM, vol. 18, no. 9, pp. 509-517, Sep. 1975.