Dimensionality Reduction for Hyperspectral Data Based on Pairwise Constraint Discriminative Analysis and Nonnegative Sparse Divergence

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 10, Issue 4, 2017, Pages 1552-1562

  Wang, Xuesong ^a   Kong, Yi ^a   Gao, Yang ^b   Cheng, Yuhu ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b Xuzhou Construction Machinery Research Institute   (China)

Author keywords

Dimensionality reduction; hyperspectral data; nonnegative sparse divergence; pairwise constraint; transfer learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); DISCRIMINANT ANALYSIS;

DIMENSIONALITY REDUCTION; HYPERSPECTRAL DATA; NON NEGATIVES; PAIRWISE CONSTRAINTS; TRANSFER LEARNING;

DATA REDUCTION;

ACCURACY ASSESSMENT; ALGORITHM; DATA PROCESSING; DISCRIMINANT ANALYSIS; IMAGE CLASSIFICATION;

EID: 85016580900     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2624303     Document Type: Article

References (53)

1. R. Heylen, M. Parente, and P. Gader, "A review of nonlinear hyperspectral unmixing methods," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 1844-1868, Jun. 2014.
2. R. Heylen and P. Scheunders, "A multilinear mixing model for nonlinear spectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 1, pp. 240-251, Jan. 2016.
3. X. Q. Lu, H. Wu, Y. Yuan, and X. Li, "Manifold regularized sparse NMF for hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2815-2826, May 2013.
4. X.Q. Lu, H. Wu, and Y. Yuan, "Double constrainedNMFfor hyperspectral spectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2746-2758, May 2014.
5. Y. Yuan, Y. C. Feng, and X. Q. Lu, "Projection-based NMF for hyperspectral unmixing," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2632-2643, Jun. 2015.
6. Y. Yuan, M. Fu, and X. Q. Lu, "Substance dependency constrained sparse NMF for hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 6, pp. 2975-2986, Jun. 2015.
7. W. H. Wang, Y. T. Qian, and Y. Y. Tang, "Hypergraph-regularized sparse NMF for Hyperspectral unmixing," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 2, pp. 681-694, Feb. 2016.
8. C. I. Chang, Hyperspectral Data Exploitation: Theory and Applications. Hoboken, NJ, USA: Wiley, 2007.
9. S. Jia, G. H. Tang, J. S. Zhu, and Q. Li, "A novel ranking-based clustering approach for hyperspectral band selection," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 1, pp. 88-102, Jan. 2016.
10. X. S. Wang, Y. Gao, and Y. H. Cheng, "A non-negative sparse semisupervised dimensionality reduction algorithm for hyperspectral data," Neurocomputing, vol. 188, pp. 275-283, 2016.
11. H. L. Yang and M. M. Crawford, "Spectral and spatial proximity-based manifold alignment for multitemporal hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 1, pp. 51-64, Jan. 2016.
12. S. C. Liu, L. Bruzzone, F. Bovolo, and P. J. Du, "Hierarchical unsupervised change detection in multitemporal hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 244-260, Jan. 2015.
13. N. Longbotham, C. Chaapel, L. Bleiler, C. Padwick, W. J. Emery, and F. Pacifici, "Very high resolution multiangle urban classification analysis," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 4, pp. 1155-1170, Apr. 2012.
14. W. A. Dorigo, "Improving the robustness of cotton status characterization by radiative transfer model inversion of multi-angular CHRIS/PROBA data," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 18-29, Feb. 2012.
15. C. Demirkesen, M. Teke, and U. Sakarya, "Hyperspectral images and LiDARbasedDEMfusion:Amulti-modal landuse classification," in Proc. Int. Geosci. Remote Sens. Symp., Quebec City, 2014, pp. 2942-2945.
16. N. Longbotham et al., "Multi-modal change detection, application to the detection of flooded areas: outcome of the 2009-2010 data fusion contest," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 331-342, Feb. 2012.
17. B. Demir, F. Bovolo, and L. Bruzzone, "Updating land-cover maps by classification of image time series: a novel change-detection-driven transfer learning approach," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 300-312, Jan. 2013.
18. D. Tuia, J. Munoz-Mari, L. Gomez-Chova, and J. Malo, "Graph matching for adaptation in remote sensing," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 329-341, Jan. 2013.
19. 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., vol. 4, no. 2, pp. 41-57, Jun. 2016.
20. L. Bruzzone and D. F. Prieto, "Unsupervised retraining of a maximumlikelihood classifier for the analysis of multitemporal remote-sensing images," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 2, pp. 456-460, Feb. 2001.
21. L. Bruzzone and R. Cossu, "A multiple cascade-classifier system for a robust a partially unsupervised updating of land-covermaps," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 9, pp. 1984-1996, Sep. 2002.
22. W. Kim and M. M. Crawford, "Adaptive classification for hyperspectral image data using manifold regularization kernel machines," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4110-4121, Nov. 2010.
23. J. M. Leiva-Murillo, L. Gómez-Chova, and G. Camps-Valls, "Multitask remote sensing data classification," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 151-161, Jan. 2013.
24. H. L. Yang and M. M. Crawford, "Domain adaptation with preservation of manifold geometry for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 2, pp. 543-555, Feb. 2016.
25. N. Alajlan, E. Pasolli, F. Melgani, and A. Franzoso, "Large-scale image classification using active learning," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 1, pp. 259-263, Jan. 2014.
26. C. Persello, A. Boularis, M. Dalponte, T. Naesset, and B. Scholkopf, "Cost-sensitive active learning with lookahead: optimizing field surveys for remote sensing data classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6652-6664, Oct. 2014.
27. M. Roy, F. Melgani, A. Ghosh, E. Blanzieri, and S. Ghosh, "Land-cover classification of remotely sensed images using compressive sensing having severe scarcity of labeled patterns," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 6, pp. 1257-1261, Jun. 2015.
28. E. Izquierdo-Verdiguier, V. Laparra, L. Gomez-Chova, and G. Gamps-Valls, "Encoding invariances in remote sensing image classification with SVM," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 5, pp. 981-985, Sep. 2013.
29. C. Persello and L. Bruzzone, "Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 5, pp. 2615-2626, May 2016.
30. G. Matasci, M. Volpi, D. Tuia, and M. Kanevski, "Transfer component analysis for domain adaptation in image classification," in Proc. 17th SPIE Image Signal Process. Remote Sens., 2011, vol. 8180, pp. 81800F- 1-81800F-9.
31. Z. Sun, C. Wang, H. Wang, and J. Li, "Learn multiple kernel SVMs for domain adaptation in hyperspectral data," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 5, pp. 1224-1228, Sep. 2013.
32. G. Matasci, M. Volpi, M. Kanevski, L. Bruzzone, and D. Tuia, "Semisupervised transfer component analysis for domain adaptation in remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 7, pp. 3550-3564, Jul. 2015.
33. H. Sun, S. Liu, S. Zhou, and H. Zhou, "Transfer sparse subspace analysis for unsupervised cross-view scene model adaptation," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 7, pp. 2901-2909, Jul. 2016.
34. S. Z. Yang, M. Lin, C. P. Hou, C. S. Hou, C. S. Zhang, and Y. Wu, "A general framework for transfer sparse subspace learning," Neural Comput. Appl., vol. 21, no. 7, pp. 1801-1817, Oct. 2012.
35. L. F. Zhang, L. P. Zhang, D. C. Tao, and X. Huang, "Sparse transfer manifold embedding for hyperspectral target detection," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 1030-1043, Feb. 2014.
36. S. Prasad and L.M. Bruce, "Limitations of subspace LDAin hyperspectral target recognition applications," in Proc. IEEE Int. Geosci. Remote Sens. Symp. 2007, pp. 4049-4052.
37. W. Bian and D. C. Tao, "Max-min distance analysis by using sequential SDP relaxation for dimension reduction," IEEE Trans. Pattern Anal.Mach. Intell., vol. 33, no. 5, pp. 1037-1050, May 2011.
38. X. T. Tao, B. Wang, and L.M. Zhang, "Orthogonal bases approach for the decomposition of mixed pixels in hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 2, pp. 219-223, Apr. 2009.
39. X. M. Tian, D. C. Tao, and Y. Rui, "Sparse transfer learning for interactive video search reranking," ACM Trans. Multimedia Comput. Commun. Appl., vol. 8, no. 3, pp. 1-19, Jul. 2012.
40. M. X. Liu and D. Q. Zhang, "Pairwise constraint-guided sparse learning for feature selection," IEEE Trans. Cybern., vol. 46, no. 1, pp. 298-310, Jan. 2016.
41. N. Y. Guan, D. C. Tao, Z. G. Luo, and B. Yuan, "Non-negative patch alignment framework," IEEE Trans. Neural Netw., vol. 22, no. 8, pp. 1218-1230, Aug. 2011.
42. Y. Gao, X. S. Wang, Y. H. Cheng, and Z. J. Wang, "Dimensionality reduction for hyperspectral data based on class-aware tensor neighborhood graph and patch alignment," IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 8, pp. 1582-1593, Aug. 2015.
43. X. G. You, W. H. Ou, C. L. P. Chen, Q. Li, Z. Q. Zhu, and Y. Y. Tang, "Robust nonnegative patch alignment for dimensionality reduction," IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 11, pp. 2260-2274, Nov. 2015.
44. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, "Domain adaptation via transfer component analysis," IEEE Trans. Neural Netw., vol. 22, no. 2, pp. 199-210, Feb. 2011.
45. W. K. Wong, "Discover latent discriminant information for dimensionality reduction: non-negative sparseness preserving," Pattern Recognit., vol. 45, no. 4, pp. 1511-1523, Apr. 2012.
46. A. M. Bruckstein, M. Elad, and M. Zibulevsky, "On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations," IEEE Trans. Inf. Theory, vol. 54, no. 11, pp. 4813-4820, Nov. 2008.
47. S. K. Sahoo and A. Makur, "Signal recovery from random measurements via extended orthogonal matching pursuit," IEEE Trans. Signal Process., vol. 63, no. 10, pp. 2572-2581, May. 2015.
48. R. He, W. S. Zheng, B. G. Hu, and X. W. Kong, "Two-stage sparse representation for robust recognition on large-scale database," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 1, pp. 35-46, Jan. 2013.
49. P. Bajorski, "Statistical inference in PCA for hyperspectral images," IEEE J. Sel. Topics Signal Process., vol. 5, no 3, pp. 438-445, Jun. 2011.
50. Q. J. Liu, L. H. Jing, M. F. Wang, and Q. Z. Lin, "Hyperspectral remote sensing image classification based onSVMoptimized by clonal selection," Spectroscopy Spectral Anal., vol. 33, no. 3, pp. 746-751, Mar. 2013.
51. K. Tan, J. P. Zhang, Q. Du, and X. S. Wang, "GPU parallel implementation of support vector machines for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 10, pp. 4647-4656, Oct. 2015.
52. L. F. Zhang, Q. Zhang, L. P. Zhang, D. C. Tao, X. Huang, and B. Du, "Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding," Pattern Recognit., vol. 48, no. 10, pp. 3102-3112, Oct. 2015.
53. L. P. Zhang, L. F. Zhang, and B. Du, "Deep learning for remote sensing data: a technical tutorial on the state of the art," IEEE Geosci. Remote Sens. Mag., vol. 4, no. 2, pp. 22-40, Jun. 2016.