Pereira_IGARSS2011_#3199.pptx

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Pereira_IGARSS2011_#3199.pptx

  1. 1. Mapping Spatial Distribution of Land Cover Classification Errors<br />Maria João Pereira, Amílcar Soares<br />CERENA – Centre for Natural Resources and Environment<br />1<br />UTL<br />
  2. 2. Introduction<br />2<br />Classificaon<br />
  3. 3. Land Cover Maps<br />3<br />
  4. 4. Confusion Matrix<br />4<br />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.<br />
  5. 5. Geostatistics<br />5<br />indicator kriging with locally varying means to integrate the image classifier’s posterior probability vectors and reference data (Kyriakidis & Dungan, 2001) <br />SIS with prediction via collocated indicator cokrigingfor updating cover type maps and for estimation of the spatial distribution of prediction errors (Magnussenand De Bruin, 2003) <br />
  6. 6. Objective<br />6<br />Mapping the spatial distribution of classification errors based on stochastic simulation and that takes into account:<br />the spatial continuity of each land cover class errors.<br />Varying errors’ patterns over the classification area<br />Classification error<br />
  7. 7. Rationale<br />7<br />Classification error<br />Class A<br />Class B<br />
  8. 8. Method<br />8<br />
  9. 9. Method<br />9<br />
  10. 10. Mapping local mean error of thematic classe i<br />10<br />Indicator kriging<br />kriging<br />weights<br />experimental data errors ei(x0)<br />Number of neibghour data<br />
  11. 11. Mapping local mean error of thematic classe i<br />11<br />
  12. 12. Mapping the spatial dispersion of classification error e(x)<br />12<br />Define a random path visiting each node u of the grid<br />For each location u along the path<br />Search conditioning data (point data and previously simulated values) and compute point-to-point covariances<br />Build and solve the kringing system conditioned to local varying means<br />Define local ccdf with its mean and variance given by the kriging estimate and variance<br />Draw a value from the ccdf and add the simulated value to data set<br />Repeat to generate another simulated realization<br />
  13. 13. Mapping the spatial dispersion of classification error e(x)<br />13<br />Mean <br />image<br />
  14. 14. Results<br />Mean<br />Variance<br />14<br />
  15. 15. Final remarks<br />15<br />Geostatistics provides na adequacte framework to assess spatial accuracy<br />In areas with field data, its influence prevails over the error trend mi(x) and vice-versa;<br />The method succeeded to map the spatial distribution of classification errors accounting for:<br />the spatial continuity of each land cover class errors.<br />Varying errors pattern over the classification area<br />
  16. 16. Thank you!<br />maria.pereira@ist.utl.pt<br />16<br />Project Landau - Contract Ref. PTDC/CTE-SPA/103872/2008<br />

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