A novel feature selection approach based on FODPSO and SVM

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 5, 2015, Pages 2935-2947

  Ghamisi, Pedram ^a   Couceiro, Micael S ^b,c   Benediktsson, Jon Atli ^a  

^a UNIVERSITY OF ICELAND   (Iceland)

^b Lda Ltd   (Portugal)

^c POLYTECHNIC INSTITUTE OF COIMBRA   (Portugal)

Author keywords

Attribute profile (AP); automatic classification; feature extraction; hyperspectral image analysis; random forest (RF) classifier; spectral spatial classification

Indexed keywords

DECISION TREES; PARTICLE SWARM OPTIMIZATION (PSO); SPECTROSCOPY; SUPPORT VECTOR MACHINES;

ATTRIBUTE PROFILE (AP); AUTOMATIC CLASSIFICATION; HYPERSPECTRAL IMAGE ANALYSIS; RANDOM FORESTS; SPECTRAL-SPATIAL CLASSIFICATION;

FEATURE EXTRACTION;

ACCURACY ASSESSMENT; MACHINERY; MODEL VALIDATION; NEW RECORD; OPTIMIZATION; SPATIAL ANALYSIS; SPECTRAL ANALYSIS;

EID: 84921033974     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2014.2367010     Document Type: Article

References (39)

1. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing, Hoboken, NJ, USA: Wiley, 2003.
2. P. Ghamisi, J. A. Benediktsson, and J. R. Sveinsson, "Automatic spectral-spatial classification framework based on attribute profiles and supervised feature extraction," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 9, pp. 5771-5782, Sep. 2014.
3. P. Ghamisi, J. A. Benediktsson, G. Cavallaro, and A. Plaza, "Automatic framework for spectral-spatial classification based on supervised feature extraction and morphological attribute profiles," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2147-2160, Jun. 2014.
4. G. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.
5. J. C. Russ, The Image Processing Handbook, 3rd ed. Boca Raton, FL, USA: CRC Press, 1999.
6. D. J. Wiersma and D. A. Landgrebe, "Analytical design of multispectral sensors," IEEE Trans. Geosci. Remote Sens., vol. GE-18, no. 2, pp. 180-189, Apr. 1980.
7. P. Bajcsy and P. Groves, "Methodology for hyperspectral band selection," Photogramm. Eng. Remote Sens., vol. 70, no. 7, pp. 793-802, 2004.
8. P. H. Swain, "Fundamentals of pattern recognition in remote sensing," in Remote Sensing-The Quantitative Approach, P. H. Swain and S. Davis, Eds., New York, NY, USA: McGraw-Hill, 1978.
9. K. Fukunaga, Introduction to Statistical Pattern Recognition, San Diego, CA, USA: Academic, 1990.
10. X. Jia, B. Kuo, and M. M. Crawford, "Feature mining for hyperspectral image classification," Proc. IEEE, vol. 101, no. 3, pp. 676-697, Mar. 2013.
11. Y. Bazi and F. Melgani, "Toward an optimal SVM classification system for hyperspectral remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3374-3385, Nov. 2006.
12. A. Daamouche, F.Melgani, N. Alajlan, and N. Conci, "Swarm optimization of structuring elements for VHR image classification," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1334-1338, Nov. 2013.
13. P. Ghamisi and J. A. Benediktsson, "Feature selection based on hybridization of genetic algorithm and particle swarm optimization," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2, pp. 309-313, Feb. 2015.
14. A. Paoli, F. Melgani, and E. Pasolli, "Clustering of hyperspectral images based on multiobjective particle swarm optimization," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 12, pp. 4175-4188, Dec. 2009.
15. F. vandenBergh and A. P. Engelbrecht, "A cooperative approach to particle swarm optimization," IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 225-239, Jun. 2004.
16. K. Premalatha and A. Natarajan, "Hybrid PSO and GA for global maximization," Int. J. Open Problems Comput. Math., vol. 2, no. 4, pp. 597-608, 2009.
17. J. Kennedy and R. Eberhart, "A new optimizer using particle swarm theory," Proc. IEEE 6th Int. Symp. Micro Mach. Human Sci., 1995, pp. 39-43.
18. Y. D. Valle, G. K. Venayagamoorthy, S. Mohagheghi, J. C. Hernandez, and R. Harley, "Particle swarm optimization: Basic concepts, variants and applications in power systems," IEEE Trans. Evol. Comput., vol. 12, no. 2, pp. 171-195, Apr. 2008.
19. J. Tillett, T. M. Rao, F. Sahin, R. Rao, and S. Brockport, "Darwinian particle swarm optimization," Proc. 2nd Indian Int. Conf. Artif. Intell., 2005, pp. 1-14.
20. P. Ghamisi, M. S. Couceiro, J. A. Benediktsson, and N. M. F. Ferreira, "An efficient method for segmentation of images based on fractional calculus and natural selection," Expert Syst. Appl., vol. 39, no. 16, pp. 12 407-12 417, Nov. 2012.
21. P. Ghamisi, M. S. Couceiro, N. M. F. Ferreira, and L. Kumar, "Use of Darwinian particle swarm optimization technique for the segmentation of remote sensing images," Proc. IGARSS, 2012, pp. 4295-4298.
22. M. S. Couceiro, R. P. Rocha, N. M. F. Ferreira, and J. A. T. Machado, "Introducing the fractional order Darwinian PSO," Signal, Image Video Process., Springer, no. Fractional Signals and Systems Special Issue, vol. 6, no. 3, pp. 343-350, Sep. 2012.
23. P. Ghamisi, M. S. Couceiro, F. M. Martins, and J. A. Benediktsson, "Multilevel image segmentation approach for remote sensing images based on fractional-order Darwinian particle swarm optimization," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2382-2394, May 2014.
24. M. S. Couceiro, F. M. Martins, R. P. Rocha, and N. M. Ferreira, "Mechanism and convergence analysis of a multi-robot swarm approach based on natural selection," J. Intell. Robot. Syst., vol. 76, no. 2, pp. 353-381, Nov. 2014.
25. M. A. Khanesar, M. Teshnehlab, and M. A. Shoorehdeli, "A novel binary particle swarm optimization," Proc. IEEE Mediterranean Conf. Control Autom., pp. 1-6, 2007.
26. V. N. Vapnic, Statistical Learning Theory, Hoboken, NJ:Wiley, 1998.
27. B. Scholkopf and A. J. Smola, Learning with Kernels, Cambridge, MA, USA: MIT Press, 2002.
28. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Kernel principal component analysis for the classification of hyperspectral remote-sensing data over urban areas," EURASIP J. Adv. Signal Process., vol. 2009, pp. 1-14, 2009.
29. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Evaluation of kernels for multiclass classification of hyperspectral remote sensing data," Proc. ICASSP, vol. 2, pp. 813-816, 2006.
30. C.-W. Hsu and C.-J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, May 2002.
31. P. Ghamisi, M. Dalla Mura, and J. A. Benediktsson, "A survey on spectral-spatial classification techniques based on attribute profiles," IEEE Trans. Geosci. Remote Sens., to be published.
32. M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, "Morphological attribute profiles for the analysis of very high resolution images," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3747-3762, Oct. 2010.
33. M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, "Extended profiles with morphological attribute filters for the analysis of hyperspectral data," Int. J. Remote Sens., vol. 31, no. 22, pp. 5975-5991, Jul. 2010.
34. M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 11, pp. 3804-3814, Nov. 2008.
35. J. A. Peacock, "Two-dimensional goodness-of-fit testing in astronomy," Mon. Not. R. Astronom. Soc., vol. 202, no. 3, pp. 615-627, 1983.
36. A. C. Pedrosa and S. M. A. Gama, Introducao Computacional a Probabilidade e Estatistica, Porto, Portugal: Porto Editora, 2004, p. 348.
37. J. Maroco, Anlise Estatstica com Utilizao do SPSS, Lisboa, Portugal: Edies Silabo, 2010,p. 199.
38. J. Pallant, SPSS Survival Manual, 4th ed. Kindle Edition Buckingham, U.K.: Open Univ. Press, 2011.
39. C. Lee and D. A. Landgrebe, "Feature extraction based on decision boundaries," IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 4, pp. 388-400, Apr. 1993.