Image classification using probabilistic models that reflect the internal structure of mixels

Pattern Analysis and Applications

Volumn 2, Issue 1, 1999, Pages 31-43

  Kitamoto, Asanobu ^a   Takagi, Mikio ^b,c  

^a NATIONAL INSTITUTE OF INFORMATICS   (Japan)

^b TOKYO UNIVERSITY OF SCIENCE   (Japan)

^c UNIVERSITY OF TOKYO   (Japan)

Author keywords

Beta distribution; Image classification; Mixel; Mixture density estimation; Probabilistic model; Satellite image

Indexed keywords

EID: 0033463507     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s100440050012     Document Type: Article

References (23)

1. Foody GM. Relating the land-cover composition of mixed pixels to artificial neural network classification output. Photogrammetric Engineering and Remote Sensing 1996;62(5):491-499
2. Foody GM, Cox DP. Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. International Journal of Remote Sensing 1994;15(3):619-631
3. Wang F. Fuzzy supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 1990;28(2):194-201
4. Kent JT, Mardia KV. Spatial classification using fuzzy membership models. IEEE Transactions on Pattern Analysis and Machine Intelligence 1988;10(5):659-671
5. Caillol H, Hillion A, Pieczynski W. Fuzzy random fields and unsupervised image segmentation. IEEE Transactions on Geoscience and Remote Sensing 1993;31(4):801-810
6. Tabatabai AJ, Mitchell R. Edge location to subpixel values in digital imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence 1984;6(2):188-201
7. Settle JJ, Drake NA. Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing 1993;14(6):1159-1177
8. Santago P, Gage HD. Quantification of MR brain images by mixture density and partial volume modeling. IEEE Transactions on Medical Imaging 1993;12(3):566-574
9. Choi HS, Haynor DR, Kim Y. Partial volume tissue classification of multichannel magnetic resonance-images - A mixel model. IEEE Transactions on Medical Imaging 1991;10(3):395-408
10. Bosdogianni P, Petrou M, Kittler J. Mixed pixel classification in remote sensing. SPIE Proceedings of the EUROPTO series, Image and Signal Processing for Remote Sensing, 2315 1994, pp 494-505
11. Kitamoto A, Takagi M. Image classification using a stochastic model that reflects the internal structure of mixels. In: Amin A, Dori D, Pudil P, Freeman H (eds), Advances in Pattern Recognition, Lecture Notes in Computer Science 1451 Springer-Verlag, 1998, pp 630-639
12. Falls LW. The beta distribution: A statistical model for world cloud cover. Journal of Geophysical Research 1974;79(9):1261-1264
13. Luo G. Simulations exploring the dependence of cloud-cover frequency distribution on cloud size and image pixel resolution. Journal of Atmospheric and Oceanic Technology 1995;12:712-720
14. Kitamoto A, Takagi M. A stochastic model of mixels and image classification. Proc. of 13th International Conference on Pattern Recognition 1996, pp 745-749
15. Kitamoto A, Takagi M. Image classification method using area proportion density that reflects the internal structure of mixels. Transactions of the IEICE 1998;J81-D-II(11):2582-2597 (in Japanese)
16. Lovejoy S. Area-perimeter relation for rain and cloud areas. Science 1982;216:185-187
17. Voss RF. Random fractal forgeries. In: Earnshaw RA (ed), Fundamental Algorithms for Computer Graphics Springer-Verlag, 1985, pp 805-835
18. Feller W. An Introduction to Probability Theory and Its Applications, Vol. 1. John Wiley & Sons, 1957
19. Feller W. An Introduction to Probability Theory and Its Applications, Vol. 2. John Wiley & Sons, 1966
20. Redner RA, Walker HF. Mixture densities, maximum likelihood and the EM algorithm. SIAM Review 1984;26(2).195-239
21. Schlesinger MI. Interdependence of learning and self-learning in pattern recognition. Cybernetics 1968;(2):81-87
22. Kitamoto A, Takagi M. Estimating the area proportions of mixels using mixture density estimation with mixel densities. Transactions of the IEICE 1998;J81-D-II(6):1160-1172 (in Japanese)
23. Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F. (eds), 2nd International Symposium on Information Theory 1973, pp 267-281