Knowledge discovery from multispectral satellite images

IEEE Geoscience and Remote Sensing Letters

Volumn 1, Issue 4, 2004, Pages 246-250

  Kulkarni, Arun ^a   McCaslin, Sara ^a  

^a UNIVERSITY OF TEXAS AT TYLER   (United States)

Author keywords

Fuzzy associative memory (FAM); Fuzzy neural network (FNN); Multispectral images; Rule generation; Supervised learning

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; COMPUTER SOFTWARE; ENGINEERING RESEARCH; FUZZY SETS; IMAGE SENSORS; MATHEMATICAL MODELS; NEURAL NETWORKS; REMOTE SENSING;

FUZZY ASSOCIATIVE MEMORY (FAM); FUZZY NEURAL NETWORK (FNN); MULTISPECTRAL IMAGES; RULE GENERATION; SUPERVISED LEARNING;

SATELLITE COMMUNICATION SYSTEMS;

EID: 8344247726     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2004.834593     Document Type: Article

References (15)

1. S. K. Pal and S. Mitra, "Multilayer perception, fuzzy sets, and classification," IEEE Trans. Neural Networks, vol. 3. pp. 683-697. Sept. 1992.
2. C. T. Lin and G. C. S. Lee, "Neural network based fuzzy logic control and decision system," IEEE Trans. Comput., vol. 40. pp. 1320-1336. Dec. 1991.
3. |3] J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol. 23. pp. 665-685. May-June 1993.
4. A. D. Kulkarni, G. B. Giridhar, and P. Coca. "Neural network based fuzzy logic decision systems for multispectral image analysis," Neural, Parallel Sci. Comput., vol. 3. no. 2. pp. 205-218, 1995.
5. A. D. Kulkarni and C. D. Cavanaugh, "Fuzzy neural network models for classification," Appl. Intell., vol. 12, pp. 207-215, 2000.
6. A. D. Kulkarni, Computer Vision and Fuzzy-Neurul Systems. Upper Saddle River. NJ: Prentice-Hall, 2001.
7. R. Andrews, J. Diederich, and A. B. Tickles, "Survey and critique of techniques for extracting rules from trained artificial neural networks," Knowl.-Based Syst., vol. 8. no. 6. pp. 373-389, 1995.
8. S. Mitra, S. K. Pal, and P. Mitra, "Datamining in a soft computing framework: A survey," IEEE Trans. Neural Networks, vol. 13. pp. 3-14. Jan. 2002.
9. S. Mitra and Y. Hayashi, "Neuro-fuzzy rule generation: Survey in soft computing framework," IEEE Trans. Neural Networks, vol. 11. pp. 748-768. May 2000.
10. S. Mitra and S. K. Pal, "Fuzzy multi-layer perceptron inferencing and rule generation," IEEE Trans. Neural Networks, vol. 6. pp. 51-63. Jan. 1995.
11. S. Mitra, R. K. De, and S. K. Pal, "Knowledge-based fuzzy MLP for classification and rule generation," IEEE Trans. Neural Networks, vol. 8. pp. 1338-1350. Nov. 1997.
12. L. Wang and J. M. Mendel, "Generating fuzzy rules by learning from examples," IEEE Trans. Syst., Man, Cybern., vol. 22. pp. 1414-1427. Nov.-Dec. 1992.
13. I. A. Taha and J. Ghosh, "Symbolic interpretation of artificial neural networks," IEEE Trans. Knowl. Data Eng., vol. 11, pp. 448-463, May-June 1999.
14. S. McCaslin and A. D. Kulkarni, "Knowledge discovery from multispectral satellite images," in Procs. NAFIPS-FLINT 2002 Int. Conf., New Orleans, LA, pp. 249-254.
15. R. G. Congalton, "A review of assessing the accuracy of classifications of remotely sensed data," Remote Sens. Environ., vol. 37. pp. 35-46. 1991.