Yang-IGARSS2011-1082.pptx

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Yang-IGARSS2011-1082.pptx

  1. 1. Applying Spectral Unmixing and Support Vector Machine to Airborne Hyperspectral Imagery for Detecting Giant Reed<br />Chenghai Yang1<br />John Goolsby1<br />James Everitt1<br />Qian Du2 <br />1USDA-ARS, Weslaco, Texas<br />2 Mississippi State University<br />1<br />
  2. 2. Giant Reed (Arundo donax)<br />Invasive weed in southern U.S. with densest stands in southern California and along the Rio Grande in Texas<br />Bamboo-like plant up to 10 m tall<br />Consumes more water than native vegetation<br />Threat to riparian areas and watersheds <br />Displace native vegetation, leading to the destruction of wildlife habitats<br />2<br />
  3. 3. Mapping Invasive Weeds along the Rio Grande<br />Arundo<br />
  4. 4. Biological Control<br />Difficult to control by mechanical or chemical methods<br />Biological control with Arundo wasps and scales <br />Arundo wasp from Spain has been released along the Rio Grande in Texas since July 2009<br />Arundo scale was released February 2011<br />4<br />Arundo scale<br />Arundo fly<br />Arundo leafminer<br />Arundo wasp<br />
  5. 5. Remote Sensing of Giant Reed<br />Map distribution and quantify infested areas<br />Assess biological control efficacy<br />Estimate water use/economic loss<br />Necessary for its control and management<br />Types of remote sensing imagery<br />Aerial photography<br />Airborne multispectral imagery<br />Airborne hyperspectral imagery<br />Satellite imagery<br />5<br />
  6. 6. Objectives<br />Evaluate linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) for distinguishing giant reed along the Rio Grande and compare the results with those from support vector machine (SVM)<br />6<br />
  7. 7. Study Area<br />Quemado<br />7<br />
  8. 8. 8<br />Airborne Hyperspectral Image Acquisition<br />Hyperspectral system<br /><ul><li>Spectral range: 467–932 nm
  9. 9. Swath width: 640 pixels
  10. 10. Bands: 128
  11. 11. Radiometric: 12 bit (0–4095)
  12. 12. Pixel size: 2.0 m</li></ul>Platform<br /><ul><li>Cessna 206
  13. 13. Altitude 2440 m & speed 180 km/h</li></ul>Image date<br /><ul><li>November 18, 2009
  14. 14. October 8, 2010</li></li></ul><li>9<br />Normal color and CIR composites of hyperspectral image for 2009 <br />Normal color composite<br />CIR composite<br />Arundo<br />Mixed woody<br />Mixed herbaceous<br />Bare soil<br />Water<br />
  15. 15. 10<br />Normal color and CIR composites of hyperspectral image for 2010 <br />Normal color composite<br />CIR composite<br />Arundo<br />Mixed woody<br />Mixed herbaceous<br />Bare soil<br />Water<br />
  16. 16. 11<br />Image Correction and Rectification<br />Raw image<br />Geometric correction<br /><ul><li>Reference line approach</li></ul>Rectification<br /><ul><li>Georeference images to UTM with GPS ground control points</li></ul>102 bands were used for analysis<br />Corrected image<br />
  17. 17. 12<br />Image Transformation<br />Minimum noise fraction (MNF) transformation was used to reduce spectral dimensionality and noise <br />First 30 MNF bands were selected for image classification based on eigenvalue plots and visual inspection of the MNF band images<br />
  18. 18. 13<br />Defined Classes<br />2009 (5 major classes)<br /><ul><li>Healthy Arundo
  19. 19. Moisture-stressed Arundo
  20. 20. Mixed vegetation
  21. 21. Soil/Sparse herbaceous
  22. 22. Water</li></ul>11 subclasses for classification<br />2010 (4 major classes)<br /><ul><li>Healthy Arundo
  23. 23. Mixed vegetation
  24. 24. Soil/Sparse herbaceous
  25. 25. Water
  26. 26. 11 subclasses for classification</li></li></ul><li>14<br />Supervised Classifications<br />Training samples and endmember spectra were extracted from the images for each subclass<br />Three classifiers were applied to 30-band MNF images<br /><ul><li>Linear spectral unmixing (LSU)
  27. 27. Mixture tuned matched filtering (MTMF)
  28. 28. Support vector machine (SVM)</li></ul>Abundance images were classified into subclasses based on maximum abundance values<br />Subclasses were merged into 5 major classes for 2009 and 4 classes for 2010<br />
  29. 29. 15<br />Accuracy Assessment<br />100 points in a stratified random pattern for the site<br />Error matrices for each classification<br />Overall accuracy, producer’s accuracy, user’s accuracy, and kappa coefficients<br />Kappa analysis to test each classification and the difference between any two classifications<br />
  30. 30. 16<br />Classification Maps for 2009 <br />MTMF<br />CIR<br />SVM<br />
  31. 31. 17<br />Classification Maps for 2010 <br />CIR<br />MTMF<br />SVM<br />
  32. 32. 18<br />Comparison between 2009 and 2010<br />MTMF-2009<br />SVM-2009<br />CIR-2009<br />SVM-2010<br />CIR-2010<br />MTMF-2010<br />
  33. 33. Accuracy Assessment results for classification maps (2009)<br />19<br />LSU = linear spectral unmixing, MTMF = mixture tuned matched filtering, and SVM = support vector machine. <br />
  34. 34. Accuracy Assessment results for classification maps (2010)<br />20<br />LSU = linear spectral unmixing, MTMF = mixture tuned matched filtering, and SVM = support vector machine. <br />
  35. 35. Conclusions<br />Airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping giant reed.<br />MTMF performed better than LSU for differentiating giant reed from associated cover types. <br />SVM produced the best classification results among the three classifiers examined. <br />Further research is needed to automate the identification of endmembers for speeding up the image classification process.<br />21<br />
  36. 36. 22<br />Current & Future Work<br />Assess the effectiveness of biological control agents (Arundo wasp and scale) with airborne imagery<br />Estimate ET rates of Arundo and associated vegetation<br />
  37. 37. Estimating Water Use of Giant Reed Using Remote Sensing-Based Evapotranspiration Models<br />23<br />Thermal<br />Camera<br />Arundo<br />Normal Color Image<br />Thermal Image<br />

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