mapping spatial distribution of land cover classification errors
DESCRIPTION
UTL. Mapping Spatial Distribution of Land Cover Classification Errors. Maria João Pereira, Amílcar Soares CERENA – Centre for Natural Resources and Environment. Introduction. Classificaon. Land Cover Maps. Confusion Matrix. - PowerPoint PPT PresentationTRANSCRIPT
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Mapping Spatial Distribution of Land Cover Classification Errors
Maria João Pereira, Amílcar SoaresCERENA – Centre for Natural Resources and Environment
UTL
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Introduction
Learning• Selection of training
areas• Determine
multivariate relation
Generalization
• Spatial and temporal stationarity of multivariate relation
Accuracy Assessment
• Validation data set
• Confusion matrix
Classificaon
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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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ObjectiveMapping 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 areaClassification error
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Rationale
• for each thematic class different errors occur depending on sensors and ground conditions
Assumption
Class A Class B
Classification error
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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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MethodSIS with
varying local means
Map the distribution of classification
errors
Map the associated uncertainty
indicator kirging estimation local
errors means for each thematic class
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Mapping local mean error of thematic classe i
Indicator kriging
experimental data errors ei(x0)
kriging weights
Number of neibghour data
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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 grid2. 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. Repeat to generate another simulated realization
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Mapping the spatial dispersion of classification error e(x)
Mean imag
e
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Results
Mean Variance
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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: the spatial continuity of each land cover class errors. Varying errors pattern over the classification area
