Sparse discriminant learning with ℓ1-graph for hyperspectral remote-sensing image classification

International Journal of Remote Sensing

Volumn 36, Issue 5, 2015, Pages 1307-1328

  Huang, Hong ^a   Luo, Fulin ^a   Ma, Zezhong ^b   Liu, Zhihua ^b  

^a CHONGQING UNIVERSITY   (China)

^b Land Surveying and Planning Academy of Chongqing   (China)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE CLASSIFICATION; NEAREST NEIGHBOR SEARCH; SPECTROSCOPY;

DIMENSIONALITY REDUCTION; GRAPH CONSTRUCTION; HYPERSPECTRAL IMAGE DATAS; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL REMOTE SENSING IMAGE; K-NEAREST NEIGHBOURS; SPARSE RECONSTRUCTION; SPARSE RECONSTRUCTIVE;

REMOTE SENSING;

ALGORITHM; AVIRIS; IMAGE CLASSIFICATION; MATRIX; MULTISPECTRAL IMAGE; NEAREST NEIGHBOR ANALYSIS; REMOTE SENSING;

EID: 84938397275     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2015.1009652     Document Type: Article

References (33)

1. M.Belkin,, and P.Niyogi. 2003. “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation.” Neural Computation 15: 1373–1396. doi:10.1162/089976603321780317.
2. L.L.Biehl,, and D.A.Landgrebe. 1994. An Introduction to Multispec: An Image Data Analysis Software System. West Lafayette, IN: Purdue University Research Foundation.
3. A.Castrodad,, Z.M.Xing, J.B.Greer, E.Bosch, L.Carin, and G.Sapiro. 2011. “Learning Discriminative Sparse Representations for Modeling, Source Separation, and Mapping of Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 49: 4263–4281. doi:10.1109/TGRS.2011.2163822.
4. A.Charles,, B.Olshausen, and C.Rozell. 2011. “Learning Sparse Codes for Hyperspectral Imagery.” IEEE Journal of Selected Topics in Signalling Processing 5: 963–978. doi:10.1109/JSTSP.2011.2149497.
5. Y.Chen,, N.Nasrabadi, and T.Tran. 2011. “Hyperspectral Image Classification Using Dictionary-Based Sparse Representation.” IEEE Transactions on Geoscience and Remote Sensing 49: 3973–3985. doi:10.1109/TGRS.2011.2129595.
6. 1-Graph for Image Analysis.” IEEE Transactions on Image Processing 19: 858–866. doi:10.1109/TIP.2009.2038764.
7. 1-Norm Solution Is Also the Sparsest Solution.” Communicable Pure Applications Mathematical 50: 797–829.
8. B.Du,, L.P.Zhang, L.F.Zhang, T.Chen, and K.Wu. 2012. “A Discriminative Manifold Learning Based Dimension Reduction Method for Hyperspectral Classification.” International Journal of Fuzzy Systems 14: 272–277.
9. K.Fukunaga, 1990. Introduction to Statistical Pattern Recognition. San Diego, CA: Academic Press.
10. J.Gui,, Z.N.Sun, W.Jia, R.X.Hu, Y.K.Lei, and S.W.Ji. 2012. “Discriminant Sparse Neighborhood Preserving Embedding for Face Recognition.” Pattern Recognition 45: 2884–2893. doi:10.1016/j.patcog.2012.02.005.
11. Q.Haq,, L.Tao, F.Sun, and S.Yang. 2012. “A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using A Few Labeled Samples.” IEEE Transactions on Geoscience and Remote Sensing 50: 2287–2302. doi:10.1109/TGRS.2011.2172617.
12. X.F.He,, D.Cai, S.C.Yan, and H.J.Zhang. 2005. “Neighborhood Preserving Embedding.” Proceedings of the 10th International Conference on Computer Vision, Beijng. October 17–21, 1208–1213. IEEE Computer Society.
13. X.F.He,, S.C.Yan, Y.Hu, P.Niyogi, and H.-J.Zhang. 2005. “Face Recognition Using Laplacian Faces.” IEEE Transactions on Pattern Analysis and Machine Intelligence 27: 328–340. doi:10.1109/TPAMI.2005.55.
14. S.A.Hosseini,, and H.Ghassemian. 2011. “A New Fast Algorithm for Multiclass Hyperspectral Image Classification with SVM.” International Journal of Remote Sensing 32: 8657–8683. doi:10.1080/01431161.2010.547882.
15. H.Huang, 2013. “Classification of Hyperspectral Remote-Sensing Images Based on Sparse Manifold Learning.” Journal of Applied Remote Sensing 7: 073464. doi:10.1117/1.JRS.7.073464.
16. H.Huang,, and Y.B.Huang. 2014. “Improved Discriminant Sparsity Neighborhood Preserving Embedding for Hyperspectral Image Classification.” Neurocomputing 136: 224–234. doi:10.1016/j.neucom.2014.01.010.
17. J.Li,, H.Zhang, Y.Huang, and L.Zhang. 2014. “Hyperspectral Image Classification by Nonlocal Joint Collaborative Representation with a Locally Adaptive Dictionary.” IEEE Transactions on Geoscience and Remote Sensing 52: 3707–3719. doi:10.1109/TGRS.2013.2274875.
18. J.Liu,, Z.Wu, L.Sun, Z.Wei, and L.Xiao. 2014. “Hyperspectral Image Classification Using Kernel Sparse Representation and Semilocal Spatial Graph Regularization.” IEEE Geoscience and Remote Sensing Letters 11: 1320–1324. doi:10.1109/LGRS.2013.2292831.
19. N.H.Ly,, Q.Du, and J.E.Fowler. 2014. “Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 52: 3872–3884. doi:10.1109/TGRS.2013.2277251.
20. L.S.Qiao,, S.C.Chen, and X.Y.Tan. 2010. “Sparsity Preserving Projections with Applications to Face Recognition.” Pattern Recognition 43: 331–341. doi:10.1016/j.patcog.2009.05.005.
21. S.T.Roweis,, and L.K.Saul. 2000. “Nonlinear Dimensionality Reduction by Locally Linear Embedding.” Science 290: 2323–2326. doi:10.1126/science.290.5500.2323.
22. Z.Shao,, and L.Zhang. 2014. “Sparse Dimensionality Reduction of Hyperspectral Image Based on Semi-Supervised Local Fisher Discriminant Analysis.” International Journal of Applied Earth Observation and Geoinformation 31: 122–129. doi:10.1016/j.jag.2014.03.015.
23. Q.Shi,, L.P.Zhang, and B.Du. 2013. “Semisupervised Discriminative Locally Enhanced Alignment for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 51: 4800–4815. doi:10.1109/TGRS.2012.2230445.
24. U.Srinivas,, Y.Chen, V.Monga, N.M.Nasrabadi, and T.D.Tran. 2012. “Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models.” Geoscience and Remote Sensing Letters, IEEE 10: 505–509. doi:10.1109/LGRS.2012.2211858.
25. M.Sugiyama, 2007. “Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis.” Journal of Machine Learning Research 8: 1027–1061.
26. W.Sun,, A.Halevy, J.J.Benedetto, W.Czaja, C.Chun Liu, H.B.Wu, B.Q.Shi, and W.Y.Li. 2014. “UL-Isomap Based Nonlinear Dimensionality Reduction for Hyperspectral Imagery Classification.” ISPRSJournal of Photogrammetry and Remote Sensing 89: 25–36. doi:10.1016/j.isprsjprs.2013.12.003.
27. Y.Tang,, H.Yuan, and L.Li. 2014. “Manifold-Based Sparse Representation for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 52: 7606–7618. doi:10.1109/TGRS.2014.2315209.
28. J.B.Tenenbaum,, V.D.Silva, and J.C.Langford. 2000. “A Global Geometric Framework for Nonlinear Dimensionality Reduction.” Science 290: 2319–2323. doi:10.1126/science.290.5500.2319.
29. S.C.Yan,, D.Xu, B.Y.Zhang, H.-J.Zhang, Q.Yang, and S.Lin. 2007. “Graph Embedding and Extensions: A General Framework for Dimensionality Reduction.” IEEE Transactions on Pattern Analysis and Machine Intelligence 29: 40–51. doi:10.1109/TPAMI.2007.250598.
30. F.Zang,, and J.S.ZhangJ. Sh. 2011. “Discriminative Learning by Sparse Representation for Classification.” Neurocomputing 74: 2176–2183. doi:10.1016/j.neucom.2011.02.012.
31. L.Zhang,, L.Zhang, D.Tao, and X.Huang. 2014. “Sparse Transfer Manifold Embedding for Hyperspectral Target Detection.” IEEE Transactions on Geoscience and Remote Sensing 52: 1030–1043. doi:10.1109/TGRS.2013.2246837.
32. C.H.Zhao,, X.H.Li, J.C.Ren, and S.Marshallb. 2013. “Improved Sparse Representation Using Adaptive Spatial Support for Effective Target Detection in Hyperspectral Imagery.” International Journal of Remote Sensing 34: 8669–8684. doi:10.1080/01431161.2013.845924.
33. H.Zou,, T.Hastie, and R.Tibshirani. 2006. “Sparse Principal Component Analysis.” Journal of Computational and Graphical Statistics 15: 265–286. doi:10.1198/106186006X113430.