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Land Use Mapping Using Constrained Monte Carlo Methods

Knapp, Simon Orlando

Description

We present a flexible, automated, Bayesian method designed for broad scale land use mapping. The method is based on a Monte Carlo Markov Chain and integrates a number of sources of ancillary data. It produces a probability density over a finite set of land use classes that can be used directly in further analyses or to classify individual pixels. The method assumes a multi- nomial prior over the possible land use types, and uses agricultural...[Show more]

dc.contributor.authorKnapp, Simon Orlando
dc.date.accessioned2017-11-13T01:43:36Z
dc.date.available2017-11-13T01:43:36Z
dc.identifier.otherb47392599
dc.identifier.urihttp://hdl.handle.net/1885/133595
dc.description.abstractWe present a flexible, automated, Bayesian method designed for broad scale land use mapping. The method is based on a Monte Carlo Markov Chain and integrates a number of sources of ancillary data. It produces a probability density over a finite set of land use classes that can be used directly in further analyses or to classify individual pixels. The method assumes a multi- nomial prior over the possible land use types, and uses agricultural statistics to form stochastic constraints over the total area allocated to each land use within a region. A supervised learner is then used to allocate pixels within the region, while respecting the constraints. We then extend this method in three ways. First, supplementary mapping is used to form further constraints over subsets of the original land use classes. Second, two spatial models are considered; the first considers the use of partially labelled pixels, where the labels are based on the current state of the Markov Chain, and the second assumes a Markov Random Field. Third, the form of the prior is relaxed, and the method extended to allow the creation of a time series of maps using either cascade or compound classification techniques. The methods are benchmarked against the probabilistic classifier upon which they are built and simple Bayesian modifications to the raw classifier that incorporate the same data. The techniques are demonstrated and assessed using Australian data generated by a national Land Use (LU) mapping program and show promise in many of the test regions we consider.
dc.language.isoen
dc.subjectLand Use
dc.subjectMarkov Chain Monte Carlo
dc.subjectRemote Sensing
dc.titleLand Use Mapping Using Constrained Monte Carlo Methods
dc.typeThesis (PhD)
local.contributor.supervisorRoberts, Steven
local.contributor.supervisorcontactsteven.roberts@anu.edu.au
dcterms.valid2017
local.description.notesthe author deposited 13/11/17
local.type.degreeDoctor of Philosophy (PhD)
dc.date.issued2016
local.contributor.affiliationResearch School of Finance, Actuarial Studies & Statistics, College of Business and Economics, The Australian National University
local.identifier.doi10.25911/5d70f10001561
local.mintdoimint
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