Finite Gamma mixture modelling using minimum message length inference: Application to SAR image analysis

International Journal of Remote Sensing

Volumn 30, Issue 3, 2009, Pages 771-792

  Ziou, Djemel ^a   Bouguila, Nizar ^b   Allili, Mohand Saïd ^a   El Zaart, Ali ^c  

^a UNIVERSITÉ DE SHERBROOKE   (Canada)

^b University of Concordia   (Canada)

^c KING SAUD UNIVERSITY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; MIXTURES; PROBABILITY DISTRIBUTIONS; SYNTHETIC APERTURE RADAR; UNSUPERVISED LEARNING;

CHANGE DETECTION; DETERMINING THE NUMBER OF CLUSTERS; FINITE MIXTURES; GAMMA DISTRIBUTION; GAMMA MIXTURES; MINIMUM MESSAGE LENGTHS; NUMBER OF CLUSTERS; SAR IMAGE SEGMENTATION;

RADAR IMAGING;

ESTIMATION METHOD; FINITE ELEMENT METHOD; IMAGE ANALYSIS; IMAGE PROCESSING; NUMERICAL MODEL; SYNTHETIC APERTURE RADAR;

EID: 67650686123     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160802392646     Document Type: Article

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