Applying Spectral Unmixing and Support Vector Machine to Airborne Hyperspectral Imagery for...
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Transcript of Applying Spectral Unmixing and Support Vector Machine to Airborne Hyperspectral Imagery for...
Chenghai Yang1
John Goolsby1
James Everitt1
Qian Du2
1 USDA-ARS, Weslaco, Texas2 Mississippi State University
Applying Spectral Unmixing and Support Vector Machine to
Airborne Hyperspectral Imagery for Detecting Giant Reed
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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 habitats2
Mapping Invasive Weeds along the Rio Grande
Arundo
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
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Arundo scaleArundo wasp Arundo fly Arundo leafminer
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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
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)
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Study Area
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Quemado
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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
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Normal color and CIR composites of hyperspectral image for 2009
ArundoMixed woodyMixed herbaceousBare soilWater
Normal color composite
CIR composite
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Normal color and CIR composites of hyperspectral image for 2010
ArundoMixed woodyMixed herbaceousBare soilWater
Normal color composite
CIR composite
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Image Correction and Rectification
Geometric correction Reference line
approach Rectification
Georeference images to UTM with GPS ground control points
102 bands were used for analysis
Raw image
Corrected image
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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
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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
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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
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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
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Classification Maps for 2009 MTMF
SVM
CIR
Giant reedStressed giant reedMixed vegetationSoil/sparse vegetation Water
0 250 500125 m 5
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Classification Maps for 2010 MTMF
SVM
CIR
Giant reedMixed dense vegetation Soil/Sparse vegetationWater
0 250 500125 m
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Comparison between 2009 and 2010MTMF-2009 SVM-2009CIR-2009
MTMF-2010 SVM-2010CIR-2010
Classifier Overallaccuracy
(%)
Overall kappa
Producer’s accuracy (PA,%) and user’s accuracy (UA,%)Giant reed Stressed
giant reedMixed dense vegetation
Soil/sparsevegetation
Water
PA UA PA UA PA UA PA UA PA UA
LSUMTMFSVM
81.091.095.0
0.7570.8850.936
89.396.496.4
89.3100100
88.2100100
68.285.089.5
76.088.092.0
67.978.688.5
60.075.090.0
100100100
100100100
100100100
Accuracy Assessment results for classification maps (2009)
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LSU = linear spectral unmixing, MTMF = mixture tuned matched filtering, and SVM = support vector machine.
Classifier Overallaccuracy
(%)
Overall kappa
Producer’s accuracy (PA,%) and user’s accuracy (UA,%)
Giant reed Mixed densevegetation
Soil/sparsevegetation
Water
PA UA PA UA PA UA PA UA
LSUMTMFSVM
81.091.094.0
0.7180.8670.912
83.789.891.8
89.195.7100
86.481.895.5
59.481.880.8
55.610094.4
10085.794.4
100100100
91.7100100
Accuracy Assessment results for classification maps (2010)
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LSU = linear spectral unmixing, MTMF = mixture tuned matched filtering, and SVM = support vector machine.
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.
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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
Estimating Water Use of Giant Reed Using Remote Sensing-Based Evapotranspiration Models
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ThermalCamera
Normal Color Image Thermal Image
Arundo