Satellite image classification using a classifier integration model

Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA

Volumn , Issue , 2011, Pages 90-94

  Park, Dong Chul ^a   Jeong, Taekyung ^a   Lee, Yunsik ^b   Min, Soo Young ^b  

^a Myong Ji University   (South Korea)

^b Korea Electronic Technology Institute (KETI)   (South Korea)

Author keywords

classification; classifier fusion; image data; local classifier

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; CLASSIFIER FUSION; CONFIDENCE LEVELS; FEATURE VECTORS; IMAGE DATA; INDIVIDUAL CLASSIFIERS; INTEGRATION MODELS; LOCAL CLASSIFIER; NEW SATELLITES; SATELLITE IMAGE CLASSIFICATION;

CLASSIFICATION (OF INFORMATION); IMAGE ANALYSIS;

FACE RECOGNITION;

EID: 84863258836     PISSN: 21615322     EISSN: 21615330     Source Type: Conference Proceeding    
DOI: 10.1109/AICCSA.2011.6126608     Document Type: Conference Paper

References (16)

1. H. Christensen, B. Lindberg, and O. Andersen. Employing heterogeneous information in a multi-stream framework. Proc. IEEE Int. Conf. ICASSP, 3:1571-1574, 2000.
2. S. Okawa, E. Bocchieri, and A. Potamianos. Multi-band speech recognition in noisy environments. Proc. IEEE Int. Conf. ICASSP, 2:641-644, 1998.
3. D.C. Park. Partitioned Feature-based Classifier Model. Proc. of ISSPIT, 1:412-417, 2009.
4. D.L. Hall and J. Llinas. An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1):6-23, 1997. (Pubitemid 127745544)
5. D.-C. Park. Multiple Feature-based Classifier and Its Application to Image Classification. Proc. of IEEE ICDMW, 65-71, 2010.
6. W. Fujibuchi and T. Kato. Classification of heterogeneous microarray data by maximum entropy kernel. BMC Bioinformatics, 8:267, 2007.
7. O. R. Terrades, E. Valveny, and S. Tabbone. Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework. IEEE Trans. Pat. Ana. and Mach. Intel., 31(9):1630-1644, 2009.
8. X. Zhu and R. Jin. Multiple information sources cooperative learning. Proc. of IJCAI, 1369-1375, 2009.
9. J. Ma. Content-Based Image Retrieval with HSV Color Space and Texture Features. Proc. of Int. Conf. on Web Information Systems and Mining, 61-63, 2009.
10. H. Li, G. Liu, and Z. Zhang. A new texture generation method based on pseudo-DCT coefficients. IEEE Trans. on Image Processing, 15(5):1300-1312, 2006.
11. D.-C. Park and D.M. Woo. Image Classification Using Gradient-based Fuzzy C-Means with Divergence Measure. IEEE Int. Joint Conf. on Neural Networks, 2521-2525, 2008.
12. Z.Z. Wang and J.H. Yong. Texture analysis and classification with linear regression model based on wavelet transform. IEEE Trans. on Image Processing, 17(8), 1421-1430, 2008.
13. D. Ballard and C. Brown. Computer Vision, Prentice Hall, Englewood Cliffs, N.J., 1982.
14. S. Weiss and C. Kulikowski. Computer systems that learn, San Mateo, CA: Morgan Kaufmann, 1991.
15. D.-C. Park. Centroid Neural Network for Unsupervised Competitive Learning. IEEE Trans.Neural Networks, 11(2):520-528, 2000.
16. D.-C. Park, O.H. Kwon, and J. Chung. Centroid neural network with a divergence measure for GPDF data clustering. IEEE Trans.Neural Networks, 19(6):948-957, 2008.