Abstract The characteristics of data can not be simulated in the traditional fuzzy clustering method effectively. Gaussian mixture model with neighbor constraints is introduced to solve the problem. Gaussian distribution is used to characterize the statistical characteristics of spectral measure. The correlation between the pixels and their neighborhood pixels are defined as prior probability and used as weight coefficients of each component in Gaussian mixture model. Finally, a Gaussian mixture model with neighborhood constraints in feature field is constructed. Log weighted Gaussian component in the mixture model is used as dissimilar measurement between the pixels and clusters, and a fuzzy clustering objective function is constructed based on Gaussian mixture model. Neighborhood constraints are introduced as a weight of component in traditional Gaussian mixture model and combined with fuzzy clustering method. Thus, the problem of multi-peak distribution of data is solved. Finally, the accuracy of the proposed algorithm is verified by experiments.
Fund:Supported by General Program of National Natural Science Foundation of China(No.41271435), Young Scientists Fund of National Natural Science Foundation of China(No.41301479), Natural Science Foundation of Liaoning Province(No.2015020090)
About author:: ZHAO Quanhua(Corresponding author),born in 1978, Ph.D., associate professor. Her research interests include the modeling and analysis of remote sensing image and the application of random geometry in remote sensing image processing.) ZHANG Hongyun,born in 1992,master student. Her research interests include remote sensing information extraction.) ZHAO Xuemei, born in 1989, Ph.D. candidate. Her research interests include the application of fuzzy mathematics in the modeling and analysis of remote sensing data.) LI Yu, born in 1963, Ph.D., professor. His research interests include remote sensing data processing theory and basic application research.)
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2017, Vol. 30 
