Mapping Spatial Distribution of Land Cover Classification Errors

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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

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Thank you!

maria.pereira@ist.utl.pt

Project Landau - Contract Ref. PTDC/CTE-SPA/103872/2008