Particle swarm optimization-based hyperspectral dimensionality reduction for urban land cover classification

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

Volumn 5, Issue 2, 2012, Pages 544-554

  Yang, He ^a   Du, Qian ^b   Chen, Genshe ^b  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b DCM Research Resources LLC   (United States)

Author keywords

Band selection; dimensionality reduction; hyperspectral imaging; particle swarm optimization (PSO); support vector machine (SVM); urban land cover mapping

Indexed keywords

BAND SELECTION; CLASSIFIER FUSION; COMPUTATIONAL COSTS; CRITERION FUNCTIONS; DIMENSIONALITY REDUCTION; GLOBAL OPTIMAL SOLUTIONS; HYPERSPECTRAL; HYPERSPECTRAL IMAGING; LINEAR DISCRIMINANT ANALYSIS; PARTICLE SWARM; SEARCHING STRATEGY; SVM CLASSIFICATION; URBAN LAND COVER; URBAN LAND COVER CLASSIFICATION;

IMAGE RETRIEVAL; PARTICLE SWARM OPTIMIZATION (PSO); PRINCIPAL COMPONENT ANALYSIS;

SUPPORT VECTOR MACHINES;

IMAGE CLASSIFICATION; LAND COVER; MAPPING; OPTIMIZATION; REMOTE SENSING; URBAN AREA;

EID: 84861738326     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2012.2185822     Document Type: Article

References (40)

1. S. van der Linden and P. Hostert, "The influence of urban structures on impervious surface maps from airborne hyperspectral data," Remote Sens. Environ., vol. 113, no. 11, pp. 2298-2305, 2009.
2. Q. Weng and D. A. Quattrochi, Urban Remote Sensing. Boca Raton, FL: CRC, 2006.
3. R. E. Karlsen, D. J. Gorsich, and G. R. Gerhart, "Target classification via support vector machine," Opt. Eng., vol. 39, no. 3, pp. 704-711, 2000.
4. G. Zhu and D. G. Blumberg, "Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel," Remote Sens. Environ., vol. 80, no. 2, pp. 233-240, 2002. (Pubitemid 34409346)
5. S. van der Linden,A. Janz, B. Waske, M. Eiden, and P.Hostert, "Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines," J. Appl. Remote Sens., vol. 1, p. 013543, 2007.
6. R. V. Platt and A. F. H. Goetz, "A Comparison of AVIRIS and Landsat for land use classification at the urban fringe," Photogram. Eng. Remote Sens., vol. 70, no. 7, pp. 813-819, 2004. (Pubitemid 39081752)
7. F. Dell Acqua, P. Gamba, A. Ferari, J. A. Palmason, J. A. Benediktsson, and K. Arnason, "Exploiting spectral and spatial information in hyperspectral urban data with high resolution," IEEE Geosci. Remote Sens. Lett., vol. 1, no. 4, pp. 322-326, Oct. 2004.
8. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 480-491, Mar. 2005. (Pubitemid 40320270)
9. J. Stuckens, P. R. Coppin, and M. E. Bauer, "Integrating contextual information with per-pixel classification for improved land cover classification," Remote Sens. Environ., vol. 71, no. 3, pp. 282-296, 2000. (Pubitemid 30124726)
10. T. Kasetkasem, M. K. Arora, and P. K. Varshney, "Super-resolution land cover mapping using a Markov random field based approach," Remote Sens. Environ., vol. 96, no. 3-4, pp. 302-314, 2005. (Pubitemid 40912432)
11. B. Zhang, X. Jia, Z. Chen, and Q. Tong, "A patch-based image classification by integrating hyperspectral data with GIS," Int. J. Remote Sens., vol. 27, no. 15, pp. 3337-3346, 2006. (Pubitemid 44203584)
12. X. Huang and L. Zhang, "An adaptivemean-shift analysis approach for object extraction and classification from urban hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 12, pp. 4173-4185, Dec. 2008.
13. D. Forster, Y. Buehler, and T. W. Kellenberger, "Mapping urban and peri-urban agriculture using high spatial resolution satellite data," J. Appl. Remote Sens., vol. 3, p. 033523, 2009.
14. R. L. Kettig and D. A. Landgrebe, "Classification of multispectral image data by extraction and classification of homogeneous objects," IEEE Trans. Geosci. Electron., vol. GE-14, no. 1, pp. 19-26, Jan. 1976.
15. L. Bruzzone and L. Carlin, "A multilevel context-based system for classification of very high spatial resolution images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 9, pp. 2587-2600, Sep. 2006. (Pubitemid 44321552)
16. Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, "Spectral- spatial classification of hyperspectral imagery based on partitional clustering techniques," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2973-2987, Aug. 2009.
17. H. Yang, Q. Du, and B. Ma, "Decision fusion on supervised and unsupervised classifiers for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 875-879, Oct. 2010.
18. R. C. Eberhart and J. Kennedy, "A new optimizer using particle swam theory," in Proc. 6th Int. Symp. Micromachine Human Sci., 1995, pp. 39-43.
19. R. C. Eberhart and Y. Shi, "Particle swarm optimization: Developments, applications, resources," in Proc. Congress Evolutionary Computation, 2001, pp. 81-86. (Pubitemid 32806322)
20. X. Hu and R. C. Eberhart, "Solving constrained nonlinear optimization problems with particle swarm optimization," in Proc. 6th World Multiconf. Systemics, Cybern. Informatics, 2002.
21. 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.
22. A. Ifarraguerri, "Visual method for spectral band selection," IEEE Geosci. Remote Sens. Lett., vol. 1, no. 2, pp. 101-106, Apr. 2004.
23. R. Huang and M. He, "Band selection based on feature weighting for classification of hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 2, pp. 156-159, Apr. 2005. (Pubitemid 40680165)
24. S. D. Backer, P.Kempeneers, W. Debruyn, and P. Scheunders, "A band selection technique for spectral classification," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 3, pp. 319-323, Jul. 2005. (Pubitemid 41633141)
25. N. Keshava, "Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 7, pp. 1552-1565, Jul. 2004.
26. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer, 2003.
27. P. Pudil, J. Novovicova, and J. Kittler, "Floating searchmethods in feature selection," Pattern Recognit. Lett., vol. 15, no. 11, pp. 1119-1125, 1994.
28. H. Yang, Q. Du, H. Su, and Y. Sheng, "An efficient method for supervised hyperspectral band selection," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 1, pp. 138-142, Jan. 2011.
29. B. Waske and J. A. Benediktsson, "Fusion of support vector machines for classification of multisensor Data," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 3858-3866, Dec. 2007. (Pubitemid 350157824)
30. J. A. Benediktsson and I. Kanellopoulos, "Classification ofmultisource and hyperspectral data based on decision fusion," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1367-1377, May 1999.
31. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002. (Pubitemid 34550429)
32. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. New York: Wiley-Interscience, 2003.
33. M. M. Dundar and D. A. Landgrebe, "Toward an optimal supervised classifier for the analysis of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 2, pp. 271-277, Feb. 2003.
34. J. Franke, D. A. Roberts, K. Halligan, and G.Menz, "Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments," Remote Sens. Environ., vol. 113, no. 8, pp. 1712-1723, 2009.
35. Y. Bazi and F.Melgani, "Toward an optimalSVM classification system for hyperspectral remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3374-3385, Nov. 2006. (Pubitemid 44711679)
36. S. T. Monteiro and Y. Kosugi, "A particle swarm optimization-based approach for hyperspectral band selection," in Proc. IEEE Congress Evolutionary Computation, 2007, pp. 3335-3340.
37. Y.-L. Chang, J.-P. Fang, J. A. Benediktsson, L. Chang, H. Ren, and K.-S. Chen, "Band selection for hyperspectral images based on parallel particle swarm optimization schemes," in Proc. IEEE Geosci. Remote Sens. Symp., 2009, vol. 5, pp. 84-87.
38. S. Ding and L. Chen, "Classification of hyperspectral remote sensing images with support vector machines and particle swarm optimization," in Proc. Int. Conf. Inf. Eng. Comput. Sci., 2009, no. 19-20, pp. 1-5.
39. R. Huang and X. Li, "Band selection based on evolution algorithm and sequential search for hyperspectral classification," in Proc. Int. Conf. Audio, Language and Image Process., 2008, pp. 1270-1273, (JM).
40. A. Leon-Garcia, Probability, Statistics, Random Processes for Electrical Engineering, 3rd ed. Upper Saddle River, NJ: Prentice-Hall, 2008.