Abstract
Extending on the method of regression-class mixture decomposition (RCMD), a RCMD-based feature mining model with genetic algorithm (coined RFMM-GA) is proposed in this paper for the extraction of features in complex remotely sensed images with a large proportion of noise. Within the framework of RFMM-GA, different features in the feature space correspond to different components of a mixture in which each of its components can be specified by a certain type of parametric distribution and the suitable parameter sets. The model captures nicely the overlapping and noisy conditions usually encountered in remotely sensed images. Features are successfully mined when the corresponding parameter sets are appropriately estimated. Through the embedded GA, features with the assumed components are hierarchically mined until the data set is decomposed into a group of feature patterns. Compared to conventional methods, the RFMM-GA has several distinct advantages: (1) The initial number of features does not need to be specified a priori. The procedure terminates after all relevant features have been unravelled. (2) Large proportion of noisy data in the mixture can be tolerated. (3) Parameter estimations of individual features are virtually independent of each other. (4) Variabilities in shapes and sizes of the features in the mixture are accounted for. Three experimental results on the extraction of ellipsoidal and linear features demonstrate the effectiveness of the RFMM-GA model for feature mining in noisy data with mixed feature distribution.
Similar content being viewed by others
References
D.J. Hand. “Data mining: Statistics and more?,” The American Statistician, Vol. 52:112–118, 1998.
C. Glymour, D. Madigan, D. Pregibon, and P. Smith. “Statistical themes and lessons for data mining,” Data Mining and Knowledge Discovery, Vol. 1:11–28, 1997.
A. Webb. Statistical Pattern Recognition. Arnold: London, 1999.
R.N. Dave and T. Fu. “Robust shape detection using fuzzy clustering practical applications,” Fuzzy Sets and Systems, Vol. 65:161–185, 1995.
J.M. Jolion, P. Meer, and S. Bataouche. “Robust clustering with applications in computer vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13:791–802, 1991.
J.S. Kim and H.S. Cho. “A fuzzy logic and neural network approach to boundary detection for noisy imagery,” Fuzzy Sets and Systems, Vol. 65:141–159, 1994.
S.K. Pal and D. Bhandari. “Genetic algorithms with fuzzy fitness function for object extraction using cellular networks,” Fuzzy Sets and Systems, Vol. 65:129–139, 1994.
L. Bastin. “Comparison of fuzzy c-means classification, linear mixture modeling and MLC probabilities as tools for unmixing coarse pixels,” International Journal of Remote Sensing, Vol. 18:3629–3648, 1997.
M.A. Friedl and C.E. Brodley. “Decision tree classification of land cover from remotely sensed data,” Remote Sensing of Environment, Vol. 61:399–409, 1997.
R. Lepage, R.G. Rouhana, B. St-onge, R. Noumeir, and R. Desjardins. “Cellular neural network for automated detection of geological lineaments on radarsat images,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 38:1224–1233, 2000.
N.K. Tripathi and K.V.G.K. Gokhale. “Directional morphological image transforms for lineament extraction from remotely sensed images,” International Journal of Remote Sensing, Vol. 21:3281–3292, 2000.
G.R. Dattareya and L.N. Kanal. “Estimation of mixing probabilities in multi-class finite mixtures,” IEEE Transaction on System, Man and Cybernetics, Vol. 20:149–158, 1990.
H. Derin. “Estimating components of univariate Gaussian mixtures using Prony’s methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 9:142–148, 1987.
G.J. McLachlan and K.E. Basford. Mixture Models: Inference and Applications to Clustering. Marcel Dekker: New York, 1988.
A.P. Dempster, N.M. Laird, and D.B. Rubin. “Maximum likelihood estimation from incomplete data via EM algorithm,” Journal of the Royal Statistical Society, Vol. B39:1–38, 1977.
G.J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. Wiley: New York, 1997.
L. Bruzzone, D.F. Prieto, and S.B. Serpico. “A neural–statistical approach to multitemporal and multisource remote-sensing image classification,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 37:1350–1359, 1999.
S. Tadjudin and D.A. Landgrebe. “Robust parameter estimation for mixture model,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 38:439–445, 2000.
X. Zhuang, T. Wang, and P. Zhang. “A highly robust estimator through partially likelihood function modeling and its application in computer vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14:19–35, 1992.
X. Zhuang, Y. Huang, K. Palaniappan, and Y. Zhao. “Gaussian mixture density modeling, decomposition, and applications,” IEEE Transactions on Image Processing, Vol. 5:1293–1301, 1996.
R.N. Dave and R. Krishnapuram. “Robust clustering methods: a unified view,” IEEE Transactions on Fuzzy Systems, Vol. 5:270–293, 1997.
Y. Leung, J. Ma, and W. Zhang. “A new method for mining regression classes in large data sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23:5–21, 2001.
P.J. Huber. Robust Statistics. Wiley: New York, 1981.
F.R. Hampel, E.M. Ronchetti, P.J. Rousseeuw, and W.A. Stahel. Robust Statistics: The Approach Based on Influence Functions. Wiley: New York, 1986.
J.H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press: Ann Arbor, 1975.
D. Goldberg. Genetic Algorithms. Addison-Wesley: Reading, Massachusetts, 1989.
J.A. Richards and X. Jia. Remote Sensing Digital Image Analysis: An Introduction. Springer: Berlin Heidelberg New York, 1999
J. Basak and D. Mahata. “A connectionist model for corner detection in binary and gray images,” IEEE Transactions on Neural Network, Vol. 11:1124–1132, 2000.
H.S. Wong and L. Guan. “A neural learning approach for adaptive image restoration using a fuzzy model-based network architecture,” IEEE Transactions on Neural Network, Vol. 12:516–531, 2001.
Y. Man and I. Gath. “Detection and separation of ring-shaped clusters using fuzzy clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16:855–861, 1994.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Leung, Y., Luo, JC., Ma, JH. et al. A New Method for Feature Mining in Remotely Sensed Images. Geoinformatica 10, 295–312 (2006). https://doi.org/10.1007/s10707-006-9829-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10707-006-9829-6