Bai

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Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

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Bai

  1. 1. Geo-spatial Data Analysis, Quality Assessment and Visualization Yong Ge Hexiang Bai Sanping Li Institute of Geographic Science & Natural Resources Research, Chinese Academy of Sciences, China [email_address] 6/31/2008
  2. 2. What we have faced in geomatics engineering? Multi-level Data Acquisition Multi-platform, multi-sensor, multi-resolution/scales, multi-formats, multi-owners and multi-temporal data We are drowning in data but starving for knowledge Landuse DEM
  3. 3. Geospatial Data Information Knowledge GIS Modeling Data --Knowledge
  4. 4. HEC-RAS OpenHydro ArcGIS Flood Plain Mapping Error propagation Spatial Data Analysis and Uncertainty Analysis Error Uncertainty Runoff DEM and River River Bank Data Prepro cessing SDA Output Input
  5. 5. Strategies
  6. 6. Uncertainty Analysis---Types <ul><li>Randomness ── Uncertainty of Random phenomenon </li></ul><ul><li>Fuzziness ── Uncertainty of Fuzzy concept </li></ul><ul><li>Roughness ── Uncertainty of Knowledge in </li></ul><ul><li>information system </li></ul>BG SD SM FP UA
  7. 7. Randomness, Fuzziness and Roughness in SDA SDA GIS data sources Evidence maps Modeling Posterior Prob. Fuzzy boundary Random error Missing data Randomness Fuzziness Roughness
  8. 8. Error factors Limitations of data acquisition & processing Uncertainty Analysis Input (Geospatial data) Output (Decision + Uncertainty Analysis) Population … . Temperature Data River Gauge Station DEM SDA Geospatial data Geospatial data with error Environment disturbance Fuzziness of definition Knowledge incompleteness
  9. 9. Uncertainty measurement Accuracy assessment Error propagation in spatial data Exploring and weakening uncertainty Error propagation in GIS operation Error propagation in multi-source data integration Uncertainty visualization Integration SDSS with uncertainty Uncertainty in the integration of GIS and remote sensing Uncertainty analysis in remote sensing information Attribute and positional uncertainty analysis Sensitivity analysis Uncertainty analysis in spatio-temporal data
  10. 10. Error propagation will accompany with each sub-process Uncertainty analysis and measurement will accompany with each sub-process as well  Assessment of data quality for original data  Accuracy of attribute and position Raw data  Error propagation models for data preprocessing  Assessment of data quality for preprocessed data  Accuracy of attribute and position Preprocessing  Error propagation models for data preprocessing  Assessment of data quality for preprocessed data  Accuracy of attribute and position Data analysis  Quality assessment of outcome  Accuracy of attribute and position … . Result from spatial data analysis Multi-level Geo-spatial Data Acquisition Land surveying: land use and soil type Photogrammetry and Remote sensing Mobile mapping and Mobile surveying Measured data: TINs, hydrography, stream stage and precipitation Geo-spatial Data Preprocessing Atmospheric Correction, Geometric Correction and Image enhancement Data format conversion, Projection transformation, Map edit Geo-spatial Data Analysis Classification: Statistics Methods, Neural Methods and Knowledge-based Methods Map operations: AND, OR and MULTIPLE EDA, Spatial autocorrelation, Fusion, Integration and Information extraction Quality assessment Geo-spatial data visualization Geo-spatial data analysis, quality assessment and visualization
  11. 11. Uncertainty Analysis---Measurement
  12. 12. Existing Methods <ul><li>There are two fashions to measure attribute uncertainty of classified remotely sensed imagery: (1)categorical scale;(2) pixel scale </li></ul>
  13. 13. Three Levels Measurement on Classified Remotely Sensed Imagery Pixel Class/Object Image BG SD SM FP UA
  14. 14. Measurements for Classified Pixels <ul><li>Assess the uncertainty of every classified pixel </li></ul><ul><li>Measurement indexes: Shannon Entropy , Fuzzy Entropy, probability residual , confusion index ,… </li></ul><ul><li>Shannon Entropy : </li></ul>x is a pixel and n is the number of class types BG SD SM FP UA
  15. 15. <ul><li>Rough degree of rough set X </li></ul><ul><li>Rough entropy of rough set X </li></ul><ul><li>The more rough degree/entropy is approaching to 0, the smaller the uncertainty of X is. </li></ul>Measurements for Classes or Objects BG SD SM FP | | | | 1 ) ( X P X P X A    1 ) ( 0   X A  ) | | 1 log | | | | )( ( ) ( 1     m i i i A A R U R X X E  | | log ) ( 0 U X E A   UA
  16. 16. <ul><li>Accuracy of approximation </li></ul>Measurements for an Image <ul><li>Quality of approximation </li></ul>BG SD SM FP UA
  17. 17. Example for measurement of uncertainty The study area was selected from a Landsat TM image which was taken over the Chinese Yellow River Delta on August 8, 1999. BG SD SM FP UA
  18. 18. Classification from MLC BG SD SM FP UA
  19. 19. BG SD SM FP Uncertainty for Pixels 1.00 Shannon Entropy 0.00 UA 0 Degree of Uncertainty 1 (0.00, 0.20] 2 (0.20, 0.40] 3 (0.40, 0.60] 4 (0.60, 0.80] 5 (0.80, 1.00]
  20. 20. Uncertainty Measurements for Classes or Objects BG SD SM FP UA
  21. 21. α =1.0 α =0.8 Rough Degree α =0.6 BG SD SM FP UA
  22. 22. Rough Entropy α =1.0 α =0.8 α =0.6 BG SD SM FP UA
  23. 23. <ul><li>Uncertainty Measurements for Image </li></ul>BG SD SM FP UA
  24. 24. Uncertainty Analysis---Visualization
  25. 25. Uncertainty Visualization BG SD SM FP Number Versus Graph and Map UA
  26. 26. Visualization--- Static Visualization BG SD SM FP 3D visualization of uncertainty in water class UA
  27. 27. Visualization--- Dynamic Visualization BG SD SM FP Class of water: pixels with low uncertainty degree in PCP Class of water: pixels with high uncertainty degree in PCP UA
  28. 28. Visualization--- Feature Visualization BG SD SM FP Probability Distribution Sample data clustering in 3D space Bottomland Water Urban Agriculture_1 Agriculture_2 Bareground UA
  29. 29. Software development--RASRS – a reliability assessment system for remote sensing information
  30. 30. Thanks!
  31. 31. Explanation of Threshold NEGATIVE Boundary Lower Approximation Class(x)=i P(Class(x)=i) > Threshold => Lower P(Class(x)=i) > 1-Threshold => Upper P(Class(x)=i) < 1-Threshold => Upper 0<=Threshold<=0.5

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