@article{ziou_finite_2009,

title = {Finite Gamma mixture modelling using minimum message length inference: Application to SAR image analysis},

author = {D. Ziou and N. Bouguila and M. S. Allili and A. El-Zaart},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650686123&doi=10.1080%2f01431160802392646&partnerID=40&md5=901ea39ad806dcb62cd630585469af60},

doi = {10.1080/01431160802392646},

issn = {01431161},

year  = {2009},

date = {2009-01-01},

journal = {International Journal of Remote Sensing},

volume = {30},

number = {3},

pages = {771–792},

abstract = {This paper discusses the unsupervised learning problem for finite mixtures of Gamma distributions. An important part of this problem is determining the number of clusters which best describes a set of data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of finite mixtures of Gamma distributions. The MML and other criteria in the literature are compared in terms of their ability to estimate the number of clusters in a data set. The comparison utilizes synthetic and RADARSAT SAR images. The performance of our method is also tested by contextual evaluations involving SAR image segmentation and change detection.},

note = {Publisher: Taylor and Francis Ltd.},

keywords = {Change detection, Determining the number of clusters, estimation method, finite element method, Finite mixtures, Gamma distribution, Gamma mixtures, Image analysis, Image processing, Image segmentation, Minimum message lengths, Mixtures, Number of clusters, numerical model, Probability distributions, Radar imaging, SAR image segmentation, Synthetic aperture radar, Unsupervised learning},

pubstate = {published},

tppubtype = {article}

}