Classification of hyperspectral data over urban areas using directional morphological profiles and semi-supervised feature extraction

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

Volumn 5, Issue 4, 2012, Pages 1177-1190

  Liao, Wenzhi ^a   Bellens, Rik ^a   Pižurica, Aleksandra ^a   Philips, Wilfried ^a   Pi, Youguo ^b  

^a GHENT UNIVERSITY   (Belgium)

^b SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Classification; high spatial resolution; hyperspectral data; morphological profiles; semi supervised feature extraction

Indexed keywords

CONVENTIONAL CLASSIFICATION METHODS; HIGH DIMENSIONAL DATA; HIGH RESOLUTION; HIGH SPATIAL RESOLUTION; HUGHES PHENOMENON; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA; MORPHOLOGICAL FEATURES; MORPHOLOGICAL OPENING; MORPHOLOGICAL OPERATOR; MORPHOLOGICAL PROFILE; OBJECT BOUNDARIES; OBJECT SHAPE; REDUNDANT INFORMATIONS; SEMI-SUPERVISED; STRUCTURING ELEMENT; UNDESIRABLE EFFECTS; URBAN AREAS;

CLASSIFICATION (OF INFORMATION); EXTRATERRESTRIAL ATMOSPHERES; OBSERVATORIES;

FEATURE EXTRACTION;

EFFICIENCY MEASUREMENT; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RESOLUTION; SPATIAL RESOLUTION; SPECTRAL RESOLUTION; URBAN AREA; URBAN MORPHOLOGY;

EID: 84864768761     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2012.2190045     Document Type: Article

References (40)

1. P. Soille, Morphological Image Analysis, Principles and Applications, 2nd ed. Berlin, Germany: Springer-Verlag, 2003.
2. P. Soille and M. Pesaresi, "Advances in mathematical morphology applied to geoscience and remote sensing," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 9, pp. 2042-2055, Sep. 2002. (Pubitemid 35458400)
3. M. Pesaresi and J. A. Benediktsson, "A new approach for the morphological segmentation of high-resolution satellite imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 2, pp. 309-320, Feb. 2001. (Pubitemid 32270994)
4. R. Bellens, S. Gautama, L.Martinez-Fonte, W. Philips, J. C.-W. Chan, and F. Canters, "Improved classification of VHR images of urban areas using directionalmorphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 10, pp. 2803-2812, Oct. 2008.
5. J. A. Benediktsson, J. 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)
6. A. Plaza, P. Martinez, J. Plaza, and R. M. Perez, "Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 466-479, March 2005. (Pubitemid 40320269)
7. 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. Advances in Signal Processing, vol. 2009, February 2009, 14 pages.
8. 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, Nov. 2010.
9. 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.
10. M. Dalla Mura, A. Villa, J. A. Benediktsson, J. Chanussot, and L. Bruzzone, "Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 3, pp. 541-545, May 2011.
11. J. Crespo, J. Serra, and R. Shafer, "Theoretical aspects of morphological filters by reconstruction," Signal Process., vol. 47, no. 2, pp. 201-225, Nov. 1995.
12. M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profile," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 11, pp. 3804-3814, Nov. 2008.
13. J. A. Benediktsson, M. Pesaresi, and K. Arnason, "Classification and feature extraction for remote sensing images from urban areas based on morphological transformations," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 9, pp. 1940-1949, Sep. 2003.
14. G. F. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Information Theory, vol. 14, no. 1, pp. 55-63, 1968.
15. T. Castaings, B. Waske, J. A. Benediktsson, and J. Chanussot, "On the influence of feature reduction for the classification of hyperspectral images based on the extended morphological profile," Int. J. Remote Sens., vol. 31, no. 22, pp. 5921-5939, July 2010.
16. L. Bruzzone and C. Persello, "A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 9, pp. 3180-3191, Sep. 2009.
17. M. Dalla Mura, J. Benediktsson, and L. Bruzzone, "Classification of hyperspectral images with extended attribute profiles and feature extraction techniques," in Proc. IGARSS, July 2010, pp. 76-79.
18. X. Zhu, "Semi-supervised learning literature survey," Dept. Comput. Sci., Univ. Wisconsin Madison. Madison, WI. Comput. Sci. TR 1530, Jul. 19, 2008.
19. L. Bruzzone, M. Chi, and M. Marconcini, "A novel transductive SVM for semisupervised classification of remote-sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3363-3373, Nov. 2006.
20. S. G. Chen and D. Q. Zhang, "Semisupervised dimensionality reduction with pairwise constraints for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett, vol. 8, no. 2, pp. 369-373, March 2011.
21. W. Z. Liao, A. Pizurica, W. Philips, and Y. G. Pi, "Feature extraction for hyperspectral image based on semi-supervised local discriminant analysis," in Proc. IEEE Joint Urban Remote Sensing Event (JURSE 2011), Apr. 2011, pp. 401-404.
22. C.-I. Chang and H. Ren, "An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 1044-1063, March 2000. (Pubitemid 30594774)
23. B. C. Kuo and D. A. Landgrebe, "Nonparametric weighted feature extraction for classification," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 5, pp. 1096-1105, May 2004.
24. Q. Du, "Modified Fisher's linear discriminant analysis for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 4, no. 4, pp. 503-507, Oct. 2007. (Pubitemid 47588019)
25. T. V. Bandos, L. Bruzzone, and G. Camps-Valls, "Classification of hyperspectral images with regularized linear discriminant analysis," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 862-873, March 2009.
26. B. C. Kuo, C. W. Chang, C. C. Hung, and H. P. Wang, "A modified nonparametric weight feature extraction using spatial and spectral information," in Proc. Int. Geoscience and Remote Sensing Symp., 2006, pp. 172-175.
27. B. C. Kuo, C. H. Li, and J. M. Yang, "Kernel nonparametric weighted feature extraction for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 4, pp. 1139-1155, April 2009.
28. H. Hotelling, "Analysis of a complex of statistical variables into principal components," J. Educational Psychology, vol. 24, pp. 417-441, 1933.
29. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. Geosci. Remote Sens., vol. 26, no. 1, pp. 65-74, Jan. 1988. (Pubitemid 18596008)
30. L. Ma, M. M. Crawford, and J. Tian, "Local manifold learning-based k-nearest-neighbor for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4099-4109, Nov. 2010.
31. G. Chen and S.-E. Qian, "Dimensionality reduction of hyperspectral imagery using improved locally linear embedding," J. Appl. Remote Sens., vol. 1, pp. 1-10, March 2007.
32. H. Y. Huang and B. C. Kuo, "Double nearest proportion feature extraction for hyperspectral-image classification," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4034-4046, Nov. 2010.
33. X. F. He, D. Cai, S. C. Yan, and H. J. Zhang, "Neighborhood preserving embedding," in Proc. 10th IEEE Int. Conf. Computer Vision 2005, 2005, vol. 2, pp. 1208-1213. (Pubitemid 44042318)
34. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. 11th Annual Conf. Computational Learning Theory, 1998, pp. 92-100.
35. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
36. G. Camps-Valls, T. Bandos, and D. Zhou, "Semisupervised graph-based hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3044-3054, Oct. 2007.
37. R. P. W. Duin, P. Juszczak, D. de Ridder, P. Paclik, E. Pekalska, and D. M. J. Tax, PRTools, aMatlab Toolbox for Pattern Recognition, 2004 [Online]. Available: http://www.prtools.org
38. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998. (Pubitemid 128695475)
39. C. C. Chang and C. J. Lin, LIBSVM: A Library for Support Vector Machines, 2001 [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm
40. G. M. Foody, "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens., vol. 70, no. 5, pp. 627-633, 2004. (Pubitemid 39081774)