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Detecting Giant Reed Using Spectral Unmixing and SVM

This study explores the use of spectral unmixing and support vector machine (SVM) techniques on airborne hyperspectral imagery to detect invasive giant reed. The results are compared and evaluated, providing valuable insights for control and management strategies.

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Detecting Giant Reed Using Spectral Unmixing and SVM

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  1. Applying Spectral Unmixing and Support Vector Machine to Airborne Hyperspectral Imagery for Detecting Giant Reed Chenghai Yang1 John Goolsby1 James Everitt1 Qian Du2 1USDA-ARS, Weslaco, Texas 2 Mississippi State University

  2. Giant Reed (Arundo donax) • Invasive weed in southern U.S. with densest stands in southern California and along the Rio Grande in Texas • Bamboo-like plant up to 10 m tall • Consumes more water than native vegetation • Threat to riparian areas and watersheds • Displace native vegetation, leading to the destruction of wildlife habitats

  3. Mapping Invasive Weeds along the Rio Grande Arundo

  4. Biological Control • Difficult to control by mechanical or chemical methods • Biological control with Arundo wasps and scales • Arundo wasp from Spain has been released along the Rio Grande in Texas since July 2009 • Arundo scale was released February 2011 Arundo scale Arundo fly Arundo leafminer Arundo wasp

  5. Remote Sensing of Giant Reed • Map distribution and quantify infested areas • Assess biological control efficacy • Estimate water use/economic loss • Necessary for its control and management • Types of remote sensing imagery • Aerial photography • Airborne multispectral imagery • Airborne hyperspectral imagery • Satellite imagery

  6. Objectives • 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)

  7. Study Area Quemado

  8. Airborne Hyperspectral Image Acquisition • Hyperspectral system • Spectral range: 467–932 nm • Swath width: 640 pixels • Bands: 128 • Radiometric: 12 bit (0–4095) • Pixel size: 2.0 m • Platform • Cessna 206 • Altitude 2440 m & speed 180 km/h • Image date • November 18, 2009 • October 8, 2010

  9. Normal color composite Normal color and CIR composites of hyperspectral image for 2009 CIR composite Arundo Mixed woody Mixed herbaceous Bare soil Water

  10. Normal color composite Normal color and CIR composites of hyperspectral image for 2010 CIR composite Arundo Mixed woody Mixed herbaceous Bare soil Water

  11. Image Correction and Rectification Raw image • Geometric correction • Reference line approach • Rectification • Georeference images to UTM with GPS ground control points • 102 bands were used for analysis Corrected image

  12. Image Transformation • Minimum noise fraction (MNF) transformation was used to reduce spectral dimensionality and noise • First 30 MNF bands were selected for image classification based on eigenvalue plots and visual inspection of the MNF band images

  13. Defined Classes • 2009 (5 major classes) • Healthy Arundo • Moisture-stressed Arundo • Mixed vegetation • Soil/Sparse herbaceous • Water • 11 subclasses for classification • 2010 (4 major classes) • Healthy Arundo • Mixed vegetation • Soil/Sparse herbaceous • Water • 11 subclasses for classification

  14. Supervised Classifications • Training samples and endmember spectra were extracted from the images for each subclass • Three classifiers were applied to 30-band MNF images • Linear spectral unmixing (LSU) • Mixture tuned matched filtering (MTMF) • Support vector machine (SVM) • Abundance images were classified into subclasses based on maximum abundance values • Subclasses were merged into 5 major classes for 2009 and 4 classes for 2010

  15. Accuracy Assessment • 100 points in a stratified random pattern for the site • Error matrices for each classification • Overall accuracy, producer’s accuracy, user’s accuracy, and kappa coefficients • Kappa analysis to test each classification and the difference between any two classifications

  16. Classification Maps for 2009 MTMF CIR SVM

  17. Classification Maps for 2010 CIR MTMF SVM

  18. Comparison between 2009 and 2010 CIR-2009 MTMF-2009 SVM-2009 SVM-2010 CIR-2010 MTMF-2010

  19. Accuracy Assessment results for classification maps (2009) LSU = linear spectral unmixing, MTMF = mixture tuned matched filtering, and SVM = support vector machine.

  20. Accuracy Assessment results for classification maps (2010) LSU = linear spectral unmixing, MTMF = mixture tuned matched filtering, and SVM = support vector machine.

  21. Conclusions • Airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping giant reed. • MTMF performed better than LSU for differentiating giant reed from associated cover types. • SVM produced the best classification results among the three classifiers examined. • Further research is needed to automate the identification of endmembers for speeding up the image classification process.

  22. Current & Future Work • Assess the effectiveness of biological control agents (Arundo wasp and scale) with airborne imagery • Estimate ET rates of Arundo and associated vegetation

  23. Estimating Water Use of Giant Reed Using Remote Sensing-Based Evapotranspiration Models Thermal Camera Arundo Normal Color Image Thermal Image

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