Using tri-training to exploit spectral and spatial information for hyperspectral data classification

Proceedings of International Conference on Computer Vision in Remote Sensing, CVRS 2012

Volumn , Issue , 2012, Pages 30-33

  Huang, Rui ^a   He, Wenyong ^a  

^a SHANGHAI UNIVERSITY   (China)

Author keywords

gray level co occurrence matrices (GLCM); hyperspectral data; markov random field (MRF); tri training

Indexed keywords

CLASSIFICATION ACCURACY; DIMENSIONALITY REDUCTION; GRAY-LEVEL CO-OCCURRENCE MATRIX; HYPERSPECTRAL DATA; HYPERSPECTRAL DATA CLASSIFICATION; MARKOV RANDOM FIELDS; SEMI-SUPERVISED CLASSIFICATION METHOD; SPATIAL ANALYSIS; SPATIAL FEATURES; SPATIAL INFORMATIONS; SPECTRAL DATA; TEXTURAL FEATURE; TRI-TRAINING; UNLABELED SAMPLES;

IMAGE SEGMENTATION; MARKOV PROCESSES; REMOTE SENSING;

CLASSIFICATION (OF INFORMATION);

EID: 84874438598     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVRS.2012.6421228     Document Type: Conference Paper

References (13)

1. S. Sarkar and G. Healey, "Hyperspectral texture classification using generalized markov fields," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, 2010, pp. 429-434.
2. 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, 2005.
3. T. C. Bau, S. Sarkar and G. Healey, "Hyperspectral region classification using a three-dimensional gabor filterbank," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 9, pp. 3457-3464, 2010.
4. X. Huang and L. Zhang, "A comparative study of spatial approaches for urban mapping using hyperspectral rosis images over pavia city, northern italy," International Journal of Remote Sensing, vol. 30, no. 12, pp. 3205-3221, 2009.
5. G. Reiher, X. Descombes, J. Zerubia and F. Falzon, "A gauss-markov model for hyperspectral texture analysis of urban areas," in Proc. 16th International Conference on Pattern Recognition, Quebec City, QC, Canada, 2002, pp. 692-695.
6. 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, 2008.
7. F. Dell'Acqua, P. Gamba, A. Ferrari, 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, 2004.
8. P. Gamba, F. Dell'Acqua, G. Lisini and G. Trianni, "Improved vhr urban area mapping exploiting object boundaries," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 8, pp. 2676-2682, 2007.
9. Z. Zhou and M. Li, "Tri-training: exploiting unlabeled data using three classifiers," IEEE Trans. Knowl. Data Eng., vol. 17, no. 11, pp. 1529-1541, 2005.
10. R. M. Haralick, K. Shanmugam and I. Dinstein, "Textural features for images classification," IEEE Trans. Syst., Man, Cybern., vol. 3, no. 6, pp. 610-621, 1973.
11. F. S. Cohen, Z. Fan and M. A. Patel, "Classification of rotated and scaled texture images using gaussian markov random fields," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 2, pp. 192-202, 1991.
12. S. Krishnamachari and R. Chellappa, "Multiresolution gauss-markov random field models for texture segmentation," IEEE Trans. Image Process., vol. 6, no. 2, pp. 251-267, 1997.
13. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. 11th Annual Conference on Computational Learning Theory, Madison, WI, 1998, pp. 92-100.