A maximum entropy approach to one-class classification of remote sensing imagery

International Journal of Remote Sensing

Volumn 31, Issue 8, 2010, Pages 2227-2235

  Li, Wenkai ^a   Guo, Qinghua ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTENNAS; CLASSIFICATION (OF INFORMATION); ENTROPY; PROBABILITY DISTRIBUTIONS; REMOTE SENSING;

CLASSIFICATION ACCURACY; CONDITIONAL PROBABILITIES; MAXIMUM-ENTROPY APPROACHES; ONE-CLASS CLASSIFICATION; ONE-CLASS SUPPORT VECTOR MACHINES (OCSVM); REMOTE SENSING CLASSIFICATION; REMOTE SENSING IMAGERY; SPATIAL RESOLUTION;

IMAGE CLASSIFICATION;

ACCURACY ASSESSMENT; AERIAL PHOTOGRAPH; DATA SET; IMAGE CLASSIFICATION; LAND CLASSIFICATION; MAXIMUM ENTROPY ANALYSIS; REMOTE SENSING; SPATIAL RESOLUTION;

POACEAE;

EID: 77951892433     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161003702245     Document Type: Article

References (21)

1. BYEUNGWOO, J. and LANDGREBE, D.A., 1999, Partially supervised classification using weighted unsupervised clustering. IEEE Transactions on Geoscience and Remote Sensing, 37, pp. 1073-1079.
2. CHANG, C.-C. and LIN, C.-J., 2001, LIBSVM: a library for support vector machines. Available online at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
3. DELLA PIETRA, S.,DELLA PIETRA, V. and LAFFERTY, J., 1997, Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, pp. 1-13.
4. ELITH, J., GRAHAM, C.H., ANDERSON, R.P., DUD́IK, M., FERRIER, S., GUISAN, A., HIJMANS, R.J., HUETTMANN, F., LEATHWICK, J.R., LEHMANN, A., LI, J., LOHMANN, L.G., LOISELLE, B.A., MANION, G., MORITZ, C., NAKAMURA, M., NAKAZAWA, Y., OVERTON, J.M.M., PETERSON, A.T., PHILLIPS, S.J., RICHARDSON, K., SCACHETTI PEREIRA, R., SCHAPIRE, R.E., SOBERÓN, J., WILLIAMS, S., WISZ, M.S. and ZIMMERMANN, N.E., 2006, Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29, pp. 129-151.
5. FOODY, G.M., 2000, Estimation of sub-pixel land cover composition in the presence of untrained classes. Computers & Geosciences, 26, pp. 469-478.
6. FOODY, G.M., 2004a, Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. International Journal of Remote Sensing, 25, pp. 3091-3104.
7. FOODY, G.M., 2004b, Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering & Remote Sensing, 70, pp. 627-633.
8. FOODY, G.M., MATHUR, A., SANCHEZ-HERNANDEZ, C. and BOYD, D.S., 2006, Training set size requirements for the classification of a specific class. Remote Sensing of Environment, 104, pp. 1-14.
9. GUO, Q., KELLY, M. and GRAHAM, C.H., 2005, Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling, 182, pp. 75-90.
10. JAYNES, E.T., 1957, Information theory and statistical mechanics. Physical Review, 106, pp. 620-630.
11. JAYNES, E.T., 1990, Notes on present status and future prospects. In Maximum Entropy and Bayesian Methods, W.T. Grandy and L.H. Schick (Eds.) (Dordrecht: Kluwer).
12. MANEVITZ, L.M. and YOUSEF, M., 2001, One-class SVMs for document classification. Journal of Machine Learning Research, 2, pp. 139-154.
13. MUNOZ-MARF, J., BRUZZONE, L. and CAMPS-VAILS, G., 2007, A support vector domain description approach to supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 45, pp. 2683-2692.
14. PEARSON, R.G., DAWSON, T.P. and LIU, C., 2004, Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography, 27, pp. 285-298.
15. PHILLIPS, S.J., ANDERSON, R.P. and SCHAPIRE, R.E., 2006, Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, pp. 231-259.
16. PHILLIPS, S.J., DUDÍK, M. and SCHAPIRE, R.E., 2004, A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning, Banff, Alberta, Canada. 1015412, pp. 655-662 (New York, NY: ACM).
17. SANCHEZ-HERNANDEZ, C., BOYD, D.S. and FOODY, G.M., 2007a, Mapping specific habitats from remotely sensed imagery: support vector machine and support vector data description based classification of coastal salt marsh habitats. Ecological Informatics, 2, pp. 83-88.
18. SANCHEZ-HERNANDEZ, C., BOYD, D.S. and FOODY, G.M., 2007b, One-class classification for mapping a specific land-cover class: SVDD classification of Fenland. IEEE Transactions on Geoscience and Remote Sensing, 45, pp. 1061-1073.
19. SCHÖLKOPF, B., PLATT, J.C., SHAWE-TAYLOR, J., SMOLA, A.J. and WILLIAMSON, R.C., 2001, Estimating the support of a high-dimensional distribution. Neural Computation, 13, pp. 1443-1471.
20. SHANNON, C.E., 1948, A mathematical theory of communication. The Bell System Technical Journal, 27, pp. 379-423, 623-656.
21. TAX, D.M.J. and DUIN, R.P.W., 2002, Uniform object generation for optimizing one-class classifiers. Journal of Machine Learning Research, 2, pp. 155-173.