Likelihood-based segmentation and classification of remotely sensed images: a Bayesian optimization approach for combining RS and GIS

Ali Akbar Abkar

Research output: Thesis › PhD Thesis - Research UT, graduation UT

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	University of Twente
Supervisors/Advisors	Mulder, N.J., Supervisor, External person Sharifi, M.A., Co-Supervisor
Award date	2 Sept 1999
Place of Publication	Enschede
Publisher	International Institute for Geo-Information Science and Earth Observation
Print ISBNs	90-6164-169-1
DOIs	https://doi.org/10.3990/1.9789061641698
Publication status	Published - 2 Sept 1999

Keywords

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title = "Likelihood-based segmentation and classification of remotely sensed images: a Bayesian optimization approach for combining RS and GIS",

keywords = "PGM, GIP, ADLIB-BOOK-238",

author = "Abkar, {Ali Akbar}",

year = "1999",

month = sep,

day = "2",

doi = "10.3990/1.9789061641698",

language = "English",

isbn = "90-6164-169-1",

series = "ITC Dissertation",

publisher = "International Institute for Geo-Information Science and Earth Observation",

address = "Netherlands",

type = "PhD Thesis - Research UT, graduation UT",

school = "University of Twente",

}

TY - THES

T1 - Likelihood-based segmentation and classification of remotely sensed images

T2 - a Bayesian optimization approach for combining RS and GIS

AU - Abkar, Ali Akbar

PY - 1999/9/2

Y1 - 1999/9/2

KW - PGM

KW - GIP

KW - ADLIB-BOOK-238

U2 - 10.3990/1.9789061641698

DO - 10.3990/1.9789061641698

M3 - PhD Thesis - Research UT, graduation UT

SN - 90-6164-169-1

T3 - ITC Dissertation

PB - International Institute for Geo-Information Science and Earth Observation

CY - Enschede

ER -