UTL
Mapping Spatial Distribution of
Land Cover Classification Errors
Maria João Pereira, Amílcar Soares
CERENA – Centre for Natural Resources and Environment
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Introduction
Classificaon
Learning
• Selection of training areas
• Determine multivariate
relation
Generalization
• Spatial and temporal
stationarity of multivariate
relation
Accuracy
Assessment
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• Validation data set
• Confusion matrix
Land Cover Maps
Classification errors
• mismatches
between actual
ground-based and
image derived class
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Confusion Matrix
Table 1. Confusion matrix. Class labels: A – coniferous forest; B – deciduous forest; C – grassland; D –
permanent tree crops; E– non-irrigated land; F – irrigated land; G – artificial areas; H – water; I –
maquis and mixed forest.
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Geostatistics

indicator kriging with locally
varying means to integrate
the image classifier’s
posterior probability vectors
and reference data
(Kyriakidis & Dungan, 2001)

SIS with prediction via
collocated indicator
cokriging for updating cover
type maps and for
estimation of the spatial
distribution of prediction
errors (Magnussen and De
Bruin, 2003)
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Objective
Mapping the spatial distribution of classification errors
based on stochastic simulation and that takes into
account:

the spatial continuity of each land cover class errors.

Varying errors’ patterns over the classification area
Classification error
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Rationale
Classification error
Assumption
• for each thematic class different errors occur
depending on sensors and ground conditions
Class A
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Class B
Method
1. Calculate the trend of the errors mi
2. Calculate local error e(x)
conditioned to the mean error of the
predicted class for that location and to
the neighboring error values
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Method
SIS with varying
local means
indicator kirging
estimation local errors
means for each
thematic class
Map the distribution of
classification errors
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Map the associated
uncertainty
Mapping local mean error of thematic classe i
Indicator kriging
Number of
neibghour data
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kriging
weights
experimental data
errors ei(x0)
Mapping local mean error of thematic classe i
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Mapping the spatial dispersion of
classification error e(x)
1.
Define a random path visiting each node u of the grid
2.
For each location u along the path
1.
Search conditioning data (point data and previously simulated values)
and compute point-to-point covariances
2.
Build and solve the kringing system conditioned to local varying means
3.
Define local ccdf with its mean and variance given by the kriging
estimate and variance
4.
Draw a value from the ccdf and add the simulated value to data set
3.
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Repeat to generate another simulated realization
Mapping the spatial dispersion of
classification error e(x)
Mean
image
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Results
Mean
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Variance
Final remarks

Geostatistics provides na adequacte framework to assess
spatial accuracy

In areas with field data, its influence prevails over the
error trend mi(x) and vice-versa;

The method succeeded to map the spatial distribution of
classification errors accounting for:


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the spatial continuity of each land cover class errors.
Varying errors pattern over the classification area
Project Landau - Contract Ref. PTDC/CTE-SPA/103872/2008
Thank you!
[email protected]
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