Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques

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

Volumn 11, Issue 4, 2018, Pages 1337-1347

  Sellami, Akrem ^a   Farah, Mohamed ^a   Farah, Imed Riadh ^a   Solaiman, Basel ^b  

^a UNIVERSITY OF MANOUBA   (Tunisia)

^b ECOLE DES MINES DE NANTES   (France)

Author keywords

Dimensionality reduction (DR); hyperspectral image (HSI) classification; knowledge extraction; semantic interpretation

Indexed keywords

CORRELATION METHODS; ELECTRIC CIRCUIT BREAKERS; EXTRACTION; FEATURE EXTRACTION; HYPERSPECTRAL IMAGING; IMAGE ENHANCEMENT; INDEPENDENT COMPONENT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SEMANTICS; SPECTROSCOPY; TENSILE STRESS;

ADAPTIVE BAND SELECTION; AUTOMATIC SELECTION; CLASSIFICATION ACCURACY; DIMENSIONALITY REDUCTION; HYPER-SPECTRAL IMAGERIES; KNOWLEDGE EXTRACTION; SEMANTIC INTERPRETATION; TENSOR LOCALITY PRESERVING PROJECTIONS;

IMAGE PROCESSING;

DATA INTERPRETATION; IMAGE CLASSIFICATION; KNOWLEDGE; MACHINE LEARNING; SPECTRAL ANALYSIS;

EID: 85042122639     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2018.2798661     Document Type: Article

References (44)

1. H. Huang and Y. Huang, "Improved discriminant sparsity neighborhood preserving embedding for hyperspectral image classification, " Neurocomputing, vol. 136, pp. 224-234, 2014.
2. J. Feng, L. Jiao, X. Zhang, and T. Sun, "Hyperspectral band selection based on trivariate mutual information and clonal selection, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 7, pp. 4092-4105, Jul. 2014.
3. X. Bai, Z. Guo, Y. Wang, Z. Zhang, and J. Zhou, "Semisupervised hyperspectral band selection via spectral-spatial hypergraph model, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2774-2783, Jun. 2015.
4. N. H. Ly, Q. Du, and J. E. Fowler, "Sparse graph-based discriminant analysis for hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 7, pp. 3872-3884, Jul. 2014.
5. P. Deepa and K. Thilagavathi, "Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis, " in Proc. 2nd Int. Conf. IEEE Electron. Commun. Syst., 2015, pp. 656-660.
6. X. He and P. Niyogi, "Locality preserving projections, " in Advances in Neural Information Processing Systems 16. Cambridge, MA, USA: MIT Press, 2003, pp. 153-160.
7. C. Jutten and J. Herault, "Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture, " Signal Process., vol. 24, no. 1, pp. 1-10, 1991.
8. X. Kang, X. Xiang, S. Li, and J. A. Benediktsson, "PCA-based edge-preserving features for hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., 2017, vol. 55, no. 12, pp. 7140-7151, Dec. 2017.
9. W. Li, S. Prasad, J. E. Fowler, and L. M. Bruce, "Locality-preserving dimensionality reduction and classification for hyperspectral image analysis, " IEEE Trans. Geosci. Remote Sens., vol. 50, no. 4, pp. 1185-1198, Apr. 2012.
10. M. Sugiyama, "Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis, " J. Mach. Learn. Res., vol. 8 pp. 1027-1061, May 2007.
11. B. Hou, X. Zhang, Q. Ye, and Y. Zheng, "A novel method for hyperspectral image classification based on Laplacian eigenmap pixels distributionflow, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 3, pp. 1602-1618, Jun. 2013.
12. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Kernel principal component analysis for feature reduction in hyperspectrale images analysis, " in Proc. IEEE 7th Nordic Signal Process. Symp., 2006, pp. 238-241.
13. Y. Zhou, J. Peng, and C. P. Chen, "Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 2, pp. 1082-1095, Feb. 2015.
14. B. Sun, X. Kang, S. Li, and J. A. Benediktsson, "Random-walker-based collaborative learning for hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 55, no. 1, pp. 212-222, Jan. 2017.
15. T. Lin and S. Bourennane, "Hyperspectral image processing by jointly filtering wavelet component tensor, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 6, pp. 3529-3541, Jun. 2013.
16. S. Velasco-Forero and J. Angulo, "Classification of hyperspectral images by tensor modeling and additive morphological decomposition, " Pattern Recog., vol. 46, no. 2, pp. 566-577, 2013.
17. N. Renard and S. Bourennane, "Dimensionality reduction based on tensor modeling for classification methods, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 4, pp. 1123-1131, Apr. 2009.
18. A. Sellami and I. R. Farah, "High-level hyperspectral image classification based on spectro-spatial dimensionality reduction, " Spatial Statist., vol. 16, pp. 103-117, 2016.
19. J. An, X. Zhang, and L. Jiao, "Dimensionality reduction based on groupbased tensor model for hyperspectral image classification, " IEEE Geosci. Remote Sens. Lett., vol. 13, no. 10, pp. 1497-1501, Oct. 2016.
20. X. Jia, B.-C. Kuo, andM.M. Crawford, "Feature mining for hyperspectral image classification, " Proc. IEEE, vol. 101, no. 3, pp. 676-697, Mar. 2013.
21. K. Tan, E. Li, Q. Du, and P. Du, "Hyperspectral image classification using band selection and morphological profiles, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 1, pp. 40-48, Jan. 2014.
22. C.-I. Chang and S. Wang, "Constrained band selection for hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1575-1585, Jun. 2006.
23. L. Shen, Z. Zhu, S. Jia, J. Zhu, and Y. Sun, "Discriminative gabor feature selection for hyperspectral image classification, " IEEE Geosci. Remote Sens. Lett., vol. 10, no. 1, pp. 29-33, Jan. 2013.
24. L. Zhang, Y. Zhong, B. Huang, J. Gong, and P. Li, "Dimensionality reduction based on clonal selection for hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 4172-4186, Dec. 2007.
25. C. Yan, X. Bai, P. Ren, L. Bai, W. Tang, and J. Zhou, "Band weighting viamaximizing interclass distance for hyperspectral image classification, " IEEE Geosci. Remote Sens. Lett., vol. 13, no. 7, pp. 922-925, Jul. 2016.
26. M. Pal and G. M. Foody, "Feature selection for classification of hyperspectral data by SVM, " IEEE Trans. Geosci. Remote Sens., vol. 48, no. 5, pp. 2297-2307, May 2010.
27. K. Sun, X. Geng, L. Ji, and Y. Lu, "A new band selection method for hyperspectral image based on data quality, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2697-2703, Jun. 2014.
28. P. Mitra, C. Murthy, and S. K. Pal, "Unsupervised feature selection using feature similarity, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 301-312, Mar. 2002.
29. Q. Du and H. Yang, "Similarity-based unsupervised band selection for hyperspectral image analysis, " IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, pp. 564-568, Oct. 2008.
30. C.-I. Chang and K.-H. Liu, "Progressive band selection of spectral unmixing for hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 4, pp. 2002-2017, Apr. 2014.
31. A. MartÍnez-UsÓMartinez-Uso, F. Pla, J.M. Sotoca, and P. Garćia-Sevilla, "Clustering-based hyperspectral band selection using information measures, " IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 4158-4171, Dec. 2007.
32. Y. Qian, F. Yao, and S. Jia, "Band selection for hyperspectral imagery using affinity propagation, " IET Comput. Vis., vol. 3, no. 4, pp. 213-222, 2009.
33. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, "An efficient k-means clustering algorithm: Analysis and implementation, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 881-892, Jul. 2002.
34. T. Pinto, T. M. Sousa, I. Praça, Z. Vale, and H. Morais, "Support vector machines for decision support in electricity markets strategic bidding, " Neurocomputing, vol. 172, pp. 438-445, 2016.
35. J. Diederich, Rule Extraction FromSupport VectorMachines, vol. 80. New York, NY, USA: Springer-Verlag, 2008.
36. H. Du, H. Qi, X. Wang, R. Ramanath, and W. E. Snyder, "Band selection using independent component analysis for hyperspectral image processing, " in Proc. 32nd IEEE Appl. Imagery Pattern Recog. Workshop, 2003, pp. 93-98.
37. B. Qi, C. Zhao, and G. Yin, "Feature weighting algorithms for classification of hyperspectral images using a support vector machine, " Appl. Opt., vol. 53, no. 13, pp. 2839-2846, 2014.
38. M. Imani and H. Ghassemian, "Feature extraction using weighted training samples, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 7, pp. 1387-1391, Jul. 2015.
39. R. Real and J. M. Vargas, "The probabilistic basis of Jaccards index of similarity, " Syst. Biol., vol. 45, no. 3, pp. 380-385, 1996.
40. L. I. Kuncheva, "A stability index for feature selection." in Artificial Intelligence and Applications. New York, NY, USA: Springer-Verlag, 2007, pp. 421-427.
41. D. Pascual, F. Pla, and J. S. Śanchez, "Cluster validation using information stability measures, " Pattern Recognit. Lett., vol. 31, no. 6, pp. 454-461, 2010.
42. G. Gulgezen, Z. Cataltepe, and L. Yu, "Stable and accurate feature selection, " in Proc. Joint Eur. Conf.Mach. Learn. Knowl. DiscoveryDatabases, Springer, New York, NY, USA, 2009, pp. 455-468.
43. G. Camps-Valls, J. Mooij, and B. Scholkopf, "Remote sensing feature selection by Kernel dependence measures, " IEEE Geosci. Remote Sens. Lett., vol. 7, no. 3, pp. 587-591, Jul. 2010.
44. B. B. Damodaran, N. Courty, and S. Lefèvre, "Sparse hilbert schmidt independence criterion and Surrogate-Kernel-based feature selection for hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 55, no. 4, pp. 2385-2398, Apr. 2017.