Land Use Mapping Using Constrained Monte Carlo Methods
Date
2016
Authors
Knapp, Simon Orlando
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Abstract
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 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.
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Keywords
Land Use, Markov Chain Monte Carlo, Remote Sensing
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